landowner and hunter surveys for white-tailed deer ......landowner and hunter surveys for...

Landowner and hunter surveys for white-tailed deer management in Minnesota: factors impacting hunter access to private lands and cell-by-cell correction to reduce

mixed-mode survey sampling effects

A THESIS

SUBMITTED TO THE FACULTY OF

UNIVERSITY OF MINNESOTA

BY

Eric Michael Walberg

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF

MASTER OF SCIENCE

Louis James Cornicelli, Ph.D., Co-Adviser

David C. Fulton, Ph.D., Co-Adviser

July 2016

i

Acknowledgements

This thesis would not have been possible without the support and guidance from a

number of individuals. I need to thank my advisers, Lou Cornicelli and David Fulton,

who have provided me with the opportunities and guidance that has allowed me to earn

this degree. Their knowledge of human dimensions research has been invaluable as I

worked towards this goal and they have challenged me to improve my skills every step of

the way. I look forward to continuing to work with them in the future. Gino D’Angelo

has been a valuable committee member that has continued to ask challenging questions

and strengthened my understanding of research design. Additionally, I need to thank

Leslie McInenly, Kelsae LaSharr, and Jordan Rutter for their enthusiasm, friendship, and

willingness to help. My parents, Mike and Carol, have given me their love and support

during this whole process. To all these people and many more that I may not have

acknowledged, I thank you for everything that you have done for me and I could not have

done it without you. Additionally, I need to thank the Minnesota Department of Natural

Resources and Minnesota Cooperative Fish and Wildlife Research Unit for their funding

and support.

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Dedication

This thesis is dedicated to my wife, Jennifer, and my daughter, Savannah. My

wife has been by my side during this whole process and I would not be here today if not

for her unwavering support, friendship, and love. She has been my sounding board and

calming voice when needed. She has been the caring mother to my daughter and I thank

her for all the sacrifices that she has given for me to accomplish this achievement.

Savannah was born while I was working on this degree and watching her grow has given

me great joy. She has provided me with needed distractions from my busy schedule and I

love her more than she will ever know. I thank you both for being a part of my life.

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Table of Contents

Acknowledgements .............................................................................................................. i Dedication ........................................................................................................................... ii

Table of Contents ............................................................................................................... iii List of Tables ..................................................................................................................... iv List of Figures ..................................................................................................................... v

CHAPTER 1

Factors impacting hunter access to private lands in southeast Minnesota .......................... 1 Abstract ........................................................................................................................... 1 Introduction ..................................................................................................................... 2 Methods........................................................................................................................... 8

Research Area and Survey Methods ........................................................................... 8 Data Analysis .............................................................................................................. 9

Results ........................................................................................................................... 11 Descriptive Results ................................................................................................... 11

Posting Behavior Model ........................................................................................... 13 Discussion ..................................................................................................................... 15 Management Implications ............................................................................................. 22

CHAPTER 2

Capability of cell-by-cell correction to reduce mixed-mode sampling effects for hunter

surveys .............................................................................................................................. 33 Abstract ......................................................................................................................... 33

Introduction ................................................................................................................... 34

Conceptual Background ............................................................................................ 36 Sources of Error in Surveys ...................................................................................... 36 Survey Modes ........................................................................................................... 37

Weighting Strategies ................................................................................................. 37 Methods......................................................................................................................... 41

Study Context and Survey Methods ......................................................................... 41 Data Analysis ............................................................................................................ 41

Results ........................................................................................................................... 43 Discussion ..................................................................................................................... 46

Literature Cited ................................................................................................................. 59

Appendix A. 2012 Study of deer management on private lands in southeast Minnesota. 65

Appendix B. 2015 Survey of Minnesota Deer Hunters .................................................... 77

iv

List of Tables

Table 1.1 Results of Principal Components Analysis ...................................................... 25

Table 1.2 Cronbach’s alpha results of attitudinal variable groups to remove variables that

reduce internal consistency ............................................................................................... 26

Table 1.3 Description of all variables included in analysis .............................................. 27

Table 1.4 Posting by private landowners in southeast Minnesota by parcel size ............ 28

Table 1.5 Percentage of private landowners in southeast Minnesota that allowed hunting

access ................................................................................................................................ 29

Table 1.6 Mean number of hunters provided hunting access by landowners that allowed

hunting in southeast Minnesota ........................................................................................ 30

Table 1.7 Results of Pearson’s r bivariate correlation and logit model representing public

hunting access to private lands in southeast Minnesota .................................................... 31

Table 1.8 Results of Pearson’s r bivariate correlation and logit model representing

posting of private lands in southeast Minnesota ............................................................... 32

Table 2.1 Adjusted response rate for demographic groups (age, gender, residence) by

mailing wave and response mode-group........................................................................... 52

Table 2.2 Comparison of demographic variables by mailing wave ................................. 53

Table 2.3 Cell-by-cell correction for responses to first two survey mailings, two Internet

survey mailings (Internet mode-group) ............................................................................ 54

Table 2.4 Cell-by-cell correction for responses to first three survey mailings, two Internet

and one mail survey mailings (3-wave mode-group) ....................................................... 55

Table 2.5 Cell-by-cell correction for responses to all four survey mailings, two Internet

and two mail survey mailings (Combined mode-group) .................................................. 56

Table 2.6 Survey questions used to analyze the impacts of cell-by-cell correction ......... 57

Table 2.7 Mean values (M) for uncorrected and corrected responses to five key

management relevant questions ........................................................................................ 58

v

List of Figures

Figure 1.1 Southeast Minnesota landowner survey study area. ....................................... 24

1

CHAPTER 1

Factors impacting hunter access to private lands in southeast Minnesota

ABSTRACT White-tailed deer (Odocoileus virginianus) have important socioeconomic

and ecological impacts in the United States. Hunting is important for the effective

management of deer and relies on access to privately owned lands. A majority of

landowners allow hunting access for friends and family, however, fewer landowners

allow access to the public. We surveyed southeast Minnesota landowners in 2013 to

examine factors that influence landowners’ decision to allow hunting access to the public.

We found that landowners allowed hunting access at a high rate on both posted and non-

posted properties (89% vs 88%), however, landowners with posted properties were more

likely to restrict hunting access to family, friends, and neighbors (77% vs 69%) and less

likely to allow public hunting access (11% vs 26%). Among all landowners, hunting

access to small properties was more likely to be restricted to family, friends, and

neighbors (83%) compared to medium (74%) or large properties (60%). General public

hunters were more likely to have hunting access to large properties (27%) than medium

(17%) or small properties (10%). Landowners who own large properties present the

greatest potential for improving future public access due to the number of hunters that

can be accommodated without crowding and because they are more likely to allow

access.

KEY WORDS: deer hunting, human dimensions, hunting access, landowners, posting

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INTRODUCTION

Deer hunting in the United States (US) has significant economic, ecological, and

social implications. White-tailed deer (Odocoileus virginianus) are the most hunted big

game animal in Minnesota and the U.S. with approximately 500,000 hunters pursuing

deer each year in Minnesota (McInenly 2013) and over 10.9 million nationwide (U.S.

DOI 2014). Hunters spend $259 million each year in Minnesota on big game-related

expenditures (U.S. DOI 2013) and an estimated $16.9 billion nationwide (U.S. DOI

2014). Landowners derive benefits from deer because they enjoy watching them, receive

satisfaction from knowing the animals are there, satisfaction from providing habitat for

wildlife, and hunting deer themselves (Lacey et al. 1993). Abundant deer populations

provide plentiful recreational opportunities for hunters (Woolf and Roseberry 1998), yet

also cause serious socioeconomic and ecological concerns about deer-vehicle collisions,

forest regeneration, flora and fauna diversity, and depredation of crops (DeCalesta 1994;

Mower et al. 1997; Waller and Alverson 1997; Brown et al. 2000; Bissonette et al. 2008).

To relieve socioeconomic and ecological pressures presented by overabundant deer

herds, state wildlife agencies use regulated harvest to maintain deer densities below the

biological carrying capacity (Fieberg et al. 2010).

Hunting is the primary mechanism for controlling white-tailed deer populations

on a broad scale (Woolf and Roseberry 1998; Brown et al. 2000). In the foreseeable

future, hunting will remain the primary management mechanism because no socially or

economically acceptable alternatives currently exist (Brown et al. 2000). In Minnesota, a

majority of deer hunters pursue deer on privately owned lands (Pradhananga et al. 2013;

Schroeder and Cornicelli 2013) with 86% of all Minnesota hunters using private lands for

3

part or all of their hunting activities (U.S. DOI 2013). Private-land hunters in Minnesota

tend to change locations less frequently than public land hunters (Fulton et al. 2006).

Most hunters (91% in southeast Minnesota) use the same areas every year, with hunters

who own land tending to change locations less frequently than individuals who do not

own property (Schroeder and Cornicelli 2013). A majority of Minnesota’s forests (56%)

are publicly owned, though a large percentage is located in northern Minnesota (Miles et

al. 2011), while over 90% of land in southeast Minnesota is privately owned (LMIC

1983). The majority of Minnesota residents live in the seven county metropolitan area,

meaning that access to private lands in areas such as southeast Minnesota is particularly

important. Access to private lands in southeast Minnesota is essential for hunter

participation and maintaining deer populations at desirable population densities based on

management goals. Therefore, limited hunting access on private lands can impede the

ability of the Minnesota Department of Natural Resources (DNR) to adequately manage

the Minnesota deer population.

Access to hunting areas has an important influence on hunter participation, which

affects the ability of agencies to achieve harvest goals (Brown et al. 2000). Hunter

harvest may be sufficient to control deer populations in some areas, but hunters are

seldom dispersed such that they provide a uniform harvest across broad landscapes

(Curtis et al. 2000). Unhunted private lands can act as a refuge (Diefenbach et al. 2005;

Poudyal et al. 2012). Areas with few hunters likely serve as refugia and a portion of the

deer population is unlikely to be harvested (Diefenbach et al. 2005). Hunter access to

private lands inhabited by deer is decreasing for numerous reasons, resulting in refugia

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that affects harvest distribution and the overall ability to use hunting to control deer

populations (Brown et al. 2000).

Conover (2001) found that landowners’ perceptions of the benefits and liabilities

from wildlife and hunters, influenced landowners’ decisions to allow hunting access to

their property. In east Texas, landowners with smaller properties were more likely to

prohibit hunting because they were more likely to experience land-use conflicts and loss

of privacy (Wright et al. 1988). Land uses such as row crop agriculture and livestock

operations were often perceived by landowners to conflict with hunting (Wright et al.

1988). In Montana, Lacey et al. (1993) found that landowners with greater dependency

on agricultural income typically desired fewer deer, were more likely to indicate big

game had a harmful impact on forage and crop yields, and were more likely to allow

hunting access. Among Wisconsin woodland owners, hunting was used by a majority of

respondents to manage deer on their parcels (>90%) and hunting was the most common

method to mitigate damage from deer (Christoffel and Craven 2000).

Conover (2001) reported that landowners tolerate some wildlife damage because

of the benefits that wildlife provide, but suggested that without hunting, wildlife

populations would increase, and animals would become more habituated to humans, thus

decreasing landowners’ tolerance of wildlife. He suggested landowners could benefit

from allowing hunting access to reduce wildlife populations and change behavior of deer

so they are less likely to cause crop damage. In southeast Minnesota, crop damage is a

significant concern for landowners and wildlife managers because row crops are the main

land use in the region (Pradhananga et al. 2013).

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In the US, deer are estimated to cause $100 million in crop damage annually

(Conover 1997). Deer were found to be the main source of crop damage in Pennsylvania

(Tzilkowski et al. 2002). In Wisconsin deer caused an estimated 80% to 90% of the total

annual crop damage (Yoder 2002). Nationally, 53% of U.S. agricultural producers

indicated that the crop damage they experienced from wildlife exceeded tolerable levels

(Conover 2001). Deer often select for and consume crops because they are typically more

digestible and contain higher levels of crude protein than many native grasses and browse

species (Mould and Robbins 1982). Reports of crop damage caused by deer are directly

related to perceived deer densities and acreage allocation shifts from high-damage crops

(e.g., feed corn, soybeans, alfalfa, and sweet corn) to other crops as deer densities

increase, though this pattern can be reversed as compensation rates from wildlife damage

programs increase (Yoder 2002). Wildlife agencies can increase hunting access to private

lands by making wildlife damage abatement and compensation programs contingent on

access rules that allow more hunting on their land (Yoder 2002). Wildlife damage

programs can be used to reduce the cost of wildlife to landowners, reducing incentives to

destroy habitat and harm wildlife populations through private abatement activities (Yoder

2002).

Posting is used by landowners to inform the public that access to their property is

prohibited unless permission is granted and is done by placing signs around their property

boundaries (Snyder et al. 2008). Landowners have increasingly posted their land resulting

in decreased access to forested land (Snyder et al. 2009). Posting does not result in

absolute denial of access to private lands for hunting, but is used to control access rather

than prevent it entirely (Jagnow et al. 2006; Snyder et al. 2009). Siemer and Brown

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(1993) found that approximately 75% of hunters reported experiencing one or more

access-related problems including: finding that the land they wanted to hunt was posted,

being unable to locate landowners to ask for permission to hunt, and being denied

permission to hunt.

Negative experiences with hunters and other outdoor recreationists has been

shown to be a strong predictor of posting land (Brown et al. 1984; Wright et al. 1988;

Siemer and Brown 1993; Jagnow et al. 2006). In New York, landowners who perceived

severe deer-related problems, especially related to overpopulation, were less likely to

post their property (Lauber and Brown 2000). However, in Pennsylvania past negative

experiences with recreationalists increased posting behavior (Jagnow et al. 2006).

Landowners’ concerns about deer damage were overridden by safety and hunter behavior

concerns that resulted in landowners restricting access to their property by posting

(Jagnow et al. 2008). In both Pennsylvania and Minnesota, posting of property was

related to perceived liability from recreational users who are allowed open access onto

private property (Jagnow et al. 2008; Snyder et al. 2008). Wright et al. (2002) argued that

the perception of landowner liability appears to be greater than actual liability risks

because state recreation-use statues provide significant liability protection for

landowners. In east Texas, the major factors that influenced landowners’ decisions to

deny access were fear of being sued for injuries sustained by hunters, concerns about

hunter-induced property damages, discharge of firearms too close to buildings, livestock

protection, and inability to control actions of hunters (Wright et al. 1988).

Posting in the United States is likely to increase in the future due to increased

urbanization, along with a decreasing supply of open land (Jagnow et al. 2006), either

7

through denied hunting access or through leased property. Landowners are unlikely to be

persuaded by altruistic motives to open their land to public hunting (Deng and Munn

2015). As demonstrated in previous studies, the monetary benefits available to the

landowner through leasing might be the stimulus needed to increase access (Wright and

Fesenmaier 1988; Deng and Munn 2015).

Many landowners in Pennsylvania who posted their property still allowed

hunting, though access was often limited to family and friends (Jagnow et al. 2006). The

primary reason for forest land ownership for many individuals in Minnesota was for

hunting purposes and many believe that allowing access to others would interfere with

their own hunting enjoyment (Snyder et al. 2008). In areas with limited hunting access,

land ownership or leasing may be the only way to secure exclusive hunting access rights

(Snyder et al. 2009). Most Wisconsin landowners allowed deer hunting on their property

(93%), although only 12% would allow people other than neighbors, relatives, or friends

to hunt their property (Christoffel and Craven 2000). Social networks can play an

important role in gaining and maintaining access to private land for hunting, and local

residents usually have an easier time finding a place to hunt (Jagnow et al. 2008). In

Pennsylvania, most landowners were comfortable allowing family, friends, and/or

neighbors on their property, but were cautious and/or fearful when a stranger was on their

property (Jagnow et al. 2008). Most southeast Minnesota landowners (72%) stated that

they would allow or continue allowing other deer hunters on their property if hunters

followed the rules on their property (Pradhananga et al. 2013).

Decreased access to private lands can increase hunting pressure on public lands

and the quality of the hunter’s experience may decline (Jagnow et al. 2006). Declining

8

hunting access to private lands also results in inadequate hunting pressure and harvest

intensity on private lands, potentially resulting in refuges and limiting the effectiveness of

hunting as a management tool. Additionally, public land hunters harvest fewer deer per

hunter, especially antlerless deer (Stedman et al. 2008). Decreasing access to private

lands may exacerbate hunters’ capacity to manage deer because public land hunters seem

more at risk of dropping out of the hunting population, as they are more likely to come

from urban areas and hunt alone (Stedman et al. 2008).

The purpose of our research is to determine factors that limit public hunting

access to private lands in southeast Minnesota. We focus on “public hunters” because the

low rate of hunting access for this group presents the greatest potential for future growth

in hunting access rates. We define “public hunters” as members of the general public that

ask permission and are not the landowners’ family or friends. Our objectives were to 1)

identify factors that limit public hunting access to private lands; 2) identify the most

informative models through an a priori model selection process based on variables known

to impact hunting access; 3) assess to whom and why landowners allow public hunting on

their land; and 4) assess spatial clustering of land parcels where public hunting access is

allowed.

METHODS

Research Area and Survey Methods

The population of interest was private landowners within the southeast Minnesota

counties of Goodhue, Wabasha, Winona, and Houston (Figure 1). We defined a sampling

frame (N = 6,090) based on publicly available county property tax identification lists and

a stratified random sample of landowners owning at least 40 acres of property within the

9

study area was selected. Property size for each landowner included the total acreage

among all parcels they own within the study area. The sample was stratified into three

categories based on number of acres owned: 40 to 79 acres (small), 80 to 250 acres

(medium), and more than 250 acres (large). Hereafter property size will be referred to as

small, medium, or large. The stratification resulted in three strata and a total sample size

of 4,193 landowners.

All randomly selected landowners received self-administered mail-back

questionnaires based on an adapted Dillman’s (2009) tailored design method (Appendix

A). Approval for human subjects research received via University of Minnesota IRB

Study Number: 0609E92806. We contacted participants three times between October

2012 and January 2013 with a cover letter and full questionnaire to enhance response

rates. A shortened version of the survey questionnaire was mailed to non-respondents in

February 2013 and served as a non-response check. Data were collected from October,

2012 to March 2013.

Data Analysis

We used ArcGIS 10.2 (ESRI 2013) to create maps and perform Moran’s I spatial

autocorrelation analysis. Spatial Autocorrelation (Moran’s I) was used to analyze the

spatial distribution of parcel location based on: 1) public hunting access and 2) parcel

size. Spatial relationships among parcels were conceptualized through the use of inverse

distance, where nearby neighboring parcels have a greater influence on computations

than features far away (ESRI 2013). Euclidean distance, straight-line distance between

two points, was used to determine distance between parcels (ESRI 2013). Moran’s I

analysis produced a Moran’s Index, z-score, and p-value to determine distribution and

10

significance of parcel distribution. Moran’s Index grades the observed distribution of

parcels from -1 (dispersed) to 1 (clustered). The Moran’s Index is compared to the

expected value if randomly distributed to produce a z-score and p-value.

Program R (R Version 3.2.2, www.r-project.org, accessed 24 November 2015)

was used for subsequent statistical analyses. Data analysis methods were based on those

used by Jagnow et al. (2006) to analyze posting behavior by landowners in Pennsylvania.

Variables for analysis were selected based on literature supporting their impact on

landowners’ decisions to allow hunting access. Principal Components Analysis was

conducted on the responses to 28 attitudinal questions (7-pt Likert scale) using a Varimax

orthogonal rotation with Kaiser Normalization (Revelle 2015). Attitudinal questions were

grouped based on loading values (>0.60) and the 15 remaining questions were averaged

to create 5 variables representing hunter concerns, deer population control, hunting

tradition, individual hunter behavior, and personal responsibility to manage deer

populations (Table 1.1). Cronbach’s alpha was used to estimate internal consistency and

remove attitudinal questions that reduce the alpha value (Table 1.2).

We developed two conceptual logistic regression models that included key

variables identified in previous research to potentially impact landowners’ decision to: 1)

provide public hunting access, and 2) post their property. Model variables were divided

into four groups: 1) hunting beliefs, 2) deer population, 3) access-related concerns, and 4)

land characteristics (Table 1.3). A possible fifth group, landowner demographics, was

considered but was excluded because previous research has found these variables are not

significantly associated with posting (Brown et al. 1984; Jagnow et al. 2006). We also

found a lower percentage of demographic variables were completed and they were not

11

significantly correlated with public hunting access. The land characteristics group was

ultimately excluded from the posting logit model to focus on attitudinal variables that

impact posting. Listwise deletion was used to limit data analysis to respondents that

provided responses to all variables within the four variable groups analyzed (n = 1,690).

A Pearson’s r bivariate correlation analysis was conducted to determine the

correlation of each variable to public hunting access or posting, along with determining

the significance of the correlation. Variables without a significant correlation (p<0.05) to

hunting access or posting were excluded from their respective logit model. Remaining

significant variables were used to create nested logit models based on combinations of

the four variable groups (Independent Variables) and public hunting access or posting

(Dependent Variable). Model selection was performed using Akaike Information

Criterion (AIC) to compare 15 public hunting access logit models and 7 posting logit

models. As suggested by Burnham and Anderson (2002:131), the model with the lowest

AIC value was selected unless another model had a ΔAIC less than 2 units, included

fewer variables, and had essentially the same maximized log-likelihood value as the best

model. Logit coefficients were used to identify the significance of each variable within

the model and odds ratios (OR) were calculated to measure association between each

variable and public hunting access or posting.

RESULTS

Descriptive Results

Of the 4,193 survey questionnaires mailed, 242 were undeliverable due to being

sent to a deceased individual or invalid address. Of the remaining 3,951 surveys, a total

of 2,312 were returned, resulting in an adjusted response rate of 59%. A non-response

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check indicated that respondents were slightly older (M = 60) on average than non-

respondents (M = 57) and slightly more likely to be male (89%) than non-respondents

(79%). Overall, 40% of landowners indicated they posted their property. There was

significant difference in posting rates (χ2=20.40, p<0.001) between the three property size

categories with landowners being more likely to post small parcels (47%) than medium

(38%) or large parcels (36%, Table 1.4). Among landowners that indicated they posted

their property, 39% posted their property due to a single event with large landowners

being more likely to do so (43%) than medium (40%) or small landowners (35%, Table

1.4). The main reasons indicated for posting were to eliminate trespassing (37%) and to

control who uses their land (22%, Table 1.4). Eliminating trespassing was the only

response category that resulted in a significant difference based on property size (χ2=6.73,

p<0.05) with medium sized landowners posting (45%) for this reason more often than

small (35%) or large landowners (30%, Table 1.4). Landowners who posted their

property allowed hunting access (89%) at a similar rate as landowners who did not post

their property (88%) but were more likely to restrict hunting access to family, friends,

and neighbors (77% vs 69%) and less likely to allow public hunting access (11% vs

26%).

Hunting was allowed on most properties (88%) with 83% of landowners allowing

hunting access by family members (64%) and friends/neighbors (66%, Table 1.5).

Landowners were significantly more likely to allow hunting access (χ2=40.15, p<0.001)

on large parcels (95%) than medium (87%) or small parcels (84%, Table 1.5).

Landowners that allowed hunting access often restricted hunting access to family,

friends, and neighbors (72%). Small landowners (83%) restricted hunting access to

13

family, friends, and neighbors more often than medium (74%) or large landowners

(60%). Large landowners were more likely to allow hunting access for family, friends,

and neighbors than on medium or small parcels (Table 1.5). Landowners allowed public

hunting access on relatively few properties (18%) though landowners were significantly

more likely to allow public hunting access (χ2=74.58, p<0.001) on large properties (27%)

than medium (17%) or small properties (10%, Table 1.5).

Among landowners who allowed hunting access, the number of hunters allowed

access increased as the property size increased (small: M = 5.3, medium: M = 6.9, large:

M = 9.2, Table 1.6). Family members (small: M = 2.5, medium: M = 2.6, large: M = 3.2)

and friends/neighbors (small: M = 2.4, medium: M = 3.6, large: M = 4.6) represented a

majority of hunters that were allowed hunting access (Table 1.6). Public hunters, people

from organized hunting groups, people who lease property, and other hunters represented

less than 1 hunter/parcel (Table 1.6). The number of public hunters allowed hunting

access increased with parcel size (small: M = 0.2, medium: M = 0.5, large: M = 0.9, Table

1.6). Landowners allowed hunting access to a small number of individuals from

organized hunting groups (small: M = 0.1, medium: M = 0.1, large: M = 0.1), property

lessee’s (small: M = 0.1, medium: M = 0.2, large: M = 0.2), and other hunters (small: M =

0.0, medium: M = 0.1, large: M = 0.2, Table 1.6).

Posting Behavior Model

Spatial distribution of public hunting access was analyzed with a Moran’s I spatial

autocorrelation analysis and resulted in a Moran’s Index of 0.026 compared to an

Expected index of -0.000456. The analysis also produced a significant z-score (4.73) and

p-value (p<0.001). The Expected Index indicates that the distribution of parcels based on

14

hunting access should be slightly dispersed if randomly distributed, while the Moran’s

Index indicates that the measured distribution is slightly clustered. The significant p-

value and the positive z-score indicated that the spatial distribution of parcels based on

public hunting access was more spatially clustered than would be expected if underlying

spatial processes were random. A second Moran’s I analysis of the spatial distribution of

public hunting access by parcel size resulted in a Moran’s Index of 0.049 compared to an

Expected Index of -0.000456. The analysis produced a significant z-score (9.00) and p-

value (p<0.001), suggesting that parcels with similar public hunting access were

significantly clustered by parcel size.

Of the 17 variables analyzed, 13 had a significant bivariate correlation to public

hunting access. All of these variables had very weak correlations to public hunting access

with parcel size (r = 0.20) and family hunting tradition (r = -0.20) having the strongest

correlations (p<0.05, Table 1.7). The logit model that best represented public hunting

access in southeast Minnesota was identified using AIC and included land characteristics,

access-related concerns, and hunter-related factors (Table 1.7). The logit coefficients for

the model indicated several variables were significant: posting properties (p<0.001),

forest cover (p<0.01), parcel size (p<0.001), hunter concerns (p<0.05), hunter behavior

(p<0.001), deer management knowledge (p<0.05), and hunting tradition (p<0.001, Table

1.7). The results of the model indicated that posting (OR = 0.51), hunter concerns (OR =

0.91), and hunting tradition (OR = 0.83) reduced the likelihood of allowing public

hunting access, while increasing property size (OR = 1.78), ownership of agricultural

land (OR = 2.25), hunter behavior (OR = 1.25), and deer management knowledge (OR =

1.19) increased the likelihood of public hunting access (Table 1.7).

15

Of the 12 variables analyzed, 9 had a significant bivariate correlation to

landowners’ decision to post their property (Table 1.8). All of these variables had very

weak correlation to posting with hunter concerns (r = 0.18) and whether the landowner

hunts (r = 0.17) having the strongest correlations (Table 1.8). The logit model that best

represented posting in southeast Minnesota based on AIC included access-related

concerns, hunter-related factors, and deer population (Table 1.8). The logit model

indicated that several variables had significant logit coefficients: hunter concerns

(p<0.001), DNR pays to allow access (p<0.01), whether the landowner hunts (p<0.01),

deer management knowledge (p<0.001), personal responsibility (p<0.01), hunter density

(p<0.05), and deer population control (p<0.01, Table 1.8). Hunter concerns (OR = 1.39),

deer management knowledge (OR = 1.63), personal responsibility (OR = 1.12), and

hunter density (OR = 9.65) increased the likelihood of posting, while DNR pays to allow

access (OR = 0.91), whether the landowner hunts (OR = 0.20), and deer population

control (OR = 0.85) decreased the likelihood of posting (Table 1.8).

DISCUSSION

Due to the lack of public land in southeast Minnesota, public hunting access to

privately owned land is important for deer population management. Liberal season

structures increase the proportion of hunters that harvest antlerless deer (G.J. D’Angelo,

Minnesota Department of Natural Resources, unpublished data) potentially allowing

family and friends to effectively accomplish management goals on small properties, but

likely not producing sufficient harvests on larger properties. Additionally, liberal hunting

seasons can result in areas with higher hunter pressure being overharvested. Posting of

properties is not an accurate measure of private land being closed to hunting, however, it

16

has been used as an indicator of landowner tolerance of hunting (Brown et al. 1984). The

same landowners have also been shown to post their land more to control, rather than

prohibit, access to their land (Brown et al. 1984). Our results support these conclusions in

that while landowners allowed hunting access at a high rate on posted and non-posted

properties (89% vs 88%), posted properties were more likely to restrict hunting access to

family, friends, and neighbors (77% vs 69%) and less likely to allow public hunting

access (11% vs 26%). In New York, 79% of landowners who posted their property

allowed friends to hunt and only 7% gave permission to strangers (Lauber and Brown

2000). The high rate of hunting access on posted properties in this and other studies

indicates that posting is not equivalent with prohibiting hunting access, although posting

does restrict access for some hunters.

Among southeast Minnesota landowners, 40% stated that they posted their

property, however, the rate of posting differed by parcel size. While landowners of large

parcels were less likely to post their property, they were more likely to post due to a

single event. The main reasons for posting were to control who uses their land and to

deter trespassing. A study of northern U.S. landowners found a similar posting rate (42%,

Cordell et al. 1998), but research in Pennsylvania (69%, Jagnow et al. 2006) and New

York (83%, Lauber and Brown 2000) were found to have higher posting rates. Posting

can limit public hunting access because hunters interpret posting signs to mean that

hunting is not allowed and consequently do not seek permission to hunt those lands

(Decker and Brown 1979). Landowners often live away from their properties and are not

immediately available for people to seek permission to hunt (Brown et al. 1984),

17

although Wright et al. (1988) found that 90% of landowners in east Texas lived within 20

miles of their land and 38% resided on the property.

In the current study, most landowners allowed hunting access (88%) and a

majority allowed hunting access to family (64%) and friends (66%), but only 18%

allowed public hunting access. A study of northern U.S. states found similar results with

54% of landowners permitting hunting access to family, 55% to friends, and 16% to

strangers (Cordell et al. 1998). Among landowners who permitted hunting access in the

current study, a majority restricted hunting access to family, friends, and neighbors

(72%). Landowners of small properties were more likely to restrict hunting access to

family, friends, and neighbors (83%) compared to landowners of medium properties

(74%) or large properties (60%). Public hunters were more likely to have hunting access

to large properties (27%) than medium (17%) or small properties (10%). Owners of small

properties are less likely to allow hunting and restrict access mostly to family, friends,

and neighbors.

Landowners allowed an average of 7.14 hunters/property though a majority were

family and friends (6.29 hunters/property) and few public hunters were allowed hunting

access (0.85 hunters/property). Landowners of large properties allowed a greater number

of hunters (M = 9.16 hunters/property) than medium (M = 6.93 hunters/property) or small

properties (M = 5.25 hunters/property). The limited number of public hunters allowed

hunting access, along with the majority of properties restricting hunting access to family,

friends, and neighbors indicates that exclusive hunting access is valuable, especially

among small landowners. Landowners, especially hunters, often desire exclusive hunting

rights on their property (Jagnow et al. 2008) and exclude others due to concerns over

18

hunting interference (Gramann et al. 1985). Obtaining exclusive rights to land-based

resources, including deer, is often accomplished through owning or leasing land (Wright

et al. 1988). These owners maintain exclusive access to wildlife resources for personal

enjoyment (Wright et al. 1988) and this attitude of “protectionism” is perhaps the most

difficult access problem to resolve (WMI 1983). Opportunity for increased access for

acquaintances is unlikely because landowners’ willingness to allow access for this group

is already high, although access for public hunters is low and illustrates that access for

this group is most in need for improvement (Lauber and Brown 2000). Small and

medium sized properties are less likely to experience growth of public hunting access

because of the large number of properties that restrict hunting access to family, friends,

and neighbors. Family hunting tradition and problem behaviors from hunters are also

factors that may make it less likely that owners of small properties would allow public

hunting access.

The spatial location of properties where landowners allowed public hunting

access were more clustered than would be expected with a random distribution (z = 4.73,

p<0.001). The statistically significant results and visual examination suggests that parcel

locations are slightly clustered but the location of the parcels likely had little impact on

landowners’ decision to allow public hunting access. Clustering of similar parcel

characteristics such as land cover type (e.g., agricultural, forest) and parcel size likely

lead to the significant result of the analysis. This is supported by our findings that parcels

with similar public hunting access were clustered across the study area based on property

size (z = 9.00, p<0.001). Smaller parcels may be clustered on the landscape due to larger

parcels being divided through inheritance or sale. Smaller parcels are likely to be

19

purchased for personal hunting properties and are more likely to be posted for this reason.

Fragmented forest lands cause concerns about access to properties because some

landowners do not allow access across their land for adjacent property owners or hunters

they provide access (MN OLA 2010). If management cooperation or shared attitudes

among neighboring landowners impacted landowners’ decision to allow public hunting

access, then parcels would be expected to be clustered to a greater extent. Cooperative

management programs would be more likely to impact deer population dynamics because

they consolidate larger blocks through quality deer management or simple hunting

agreements (Christoffel and Craven 2000).

Within the public hunting access logit model, several variables were found to be

significant. Parcel posting rates, hunter concerns, and hunting tradition were found to be

inversely related to public hunting access, while property size, amount of agricultural

land, hunter behavior, and deer management knowledge was directly related to hunting

access. Ultimately parcel characteristics had the strongest impact on landowners’

decision to allow public hunting access, which suggests that the properties size and land

cover is an important factor in a landowner’s decision to allow public hunting access,

along with concerns about hunter behavior and liability. Additionally, the model shows

that knowledge about deer management in Minnesota and the landowners’ hunting

tradition were also major factors.

We created a second model to determine what factors impacted a landowners’

decision to post their properties because posting was a significant variable within our

public hunting access model and had the strongest negative impact (OR = 0.51). Our

posting logit model found several significant variables: hunter concerns, DNR pays to

20

allow access, whether the landowner hunts, deer management knowledge, personal

responsibility, hunter density, and deer population control. Hunter concerns, deer

management knowledge, personal responsibility, and hunter density were directly related

to landowners posting their properties, while DNR pays to allow access, whether the

landowner hunts, and population control were inversely related to posting. Whether the

landowner hunts and the hunter density on the property were significant factors within

the model and suggest that maintaining exclusive hunting access is a concern for

landowners, along with concerns about hunter behavior which includes liability concerns.

Past studies have shown that the fear of being sued for injuries sustained by hunters is a

major deterrent in access decisions for landowners (Wright et al. 1988), despite receiving

liability protection. In Minnesota, landowners that allow access for recreation without

charge receive liability protection, but this protection is lost if landowners charge a fee

for access (MN Statute 604A.23). In their study, Wright et al. (1988) found that 75% of

respondents did not know how much protection they had under state statutes.

Leasing is a possible way to increase availability of land open to public access,

but in our study only 4% of landowners leased their property for deer hunting. The main

reasons for leasing were to have better control over who uses their land (91%), earn extra

income from their property (86%), and they see leasing as the future way landowners can

manage their property (77%). Landowners’ beliefs about payments from the Minnesota

DNR impacting future decisions to allow deer hunting on their property was found to

significantly impact posting rates, but was not significant in the public hunting access

logit model. An important benefit for landowners leasing their land is to increase their

ability to control hunter behavior (Wright and Fesenmaier 1990). Landowners who lease

21

their land believe that leasing gives them greater control over hunters’ actions, serves as a

deterrent to trespassing, and minimizes property damage (Wright et al. 1988).

Government programs have not been found to increase availability of land through

leasing (Wright and Fesenmaier 1990). Leasing either through government programs or

private individuals is unlikely to increase because our results indicate that maintaining

exclusive hunting rights is important for landowners and this is unlikely to change.

Hunting is the only feasible method at this time for managing white-tailed deer at

landscape scales and hunting access to private lands is critical for future management

purposes. In the US, 66% of the land is under private, nonindustrial ownership and

constitutes 80% of wildlife habitats (Benson 2001). Lack of hunting access to private

lands creates refuges for deer preventing uniform harvest pressure across the landscape

which reduces the effectiveness of hunting as a management tool. Public access to private

lands is necessary to achieve harvest quotas and meet recreational demand (Brown et al.

1984). In this study, posting of properties and property size represented the most

significant factors that affected public hunting access to private land. Posting resulted in

reduced public hunting access either intentionally or unintentionally, while landowners of

large properties were more likely to allow public hunting access and more hunters

overall.

Hunter concerns (e.g., liability) and knowledge about deer management was

significant in both models, suggesting there are opportunities to educate landowners

about the importance of allowing public hunting access and liability protections available

to them. Landowners are often concerned about the liability associated with allowing

public hunting access without understanding the liability protections that are provided to

22

them under state statutes. Educating landowners about the liability protections provided

to them and ways to increase communications between landowners and hunters will be

important strategies to increase public hunting access. Another important step is to

educate hunters that landowners who post their lands are likely to allow hunting and

encourage landowners to use signs that indicate hunting may be permitted upon request

(Brown et al. 1984). Hunters also could be educated about proper hunter behavior to

avoid property damage and other behaviors that can lead to limited public hunting access.

This study suggests that focusing on posted and large properties through education efforts

will provide the greatest potential for effectively managing white-tailed deer populations

in the future.

MANAGEMENT IMPLICATIONS

Our research findings suggest that parcel characteristics are the most important

factors that impacted landowners’ decision to allow hunting access, particularly the size

of the property and whether it was posted. Posting did not have an impact on overall

hunting access but rather restricted who was allowed hunting access, favoring family,

friends, and neighbors while restricting public hunting access. We found a significant

correlation between landowners’ posting and allowing public hunting access, though this

result indicates a relationship between these variables but not necessarily causation.

Posting is likely acting as a proxy for factors that cause posting, such as the desire for

exclusive hunting access and to prevent trespassing. Posting was not equivalent to the

denial of hunting access but was a significant factor. The impacts of posting may be a

combination between landowners’ restricting access and hunters’ misperceptions about

posting. Large parcels provide the greatest potential for improving public hunting access

23

because large landowners were more likely to allow public hunting access and a greater

number of hunters can be allowed access without experiencing crowding. Important areas

of future focus will be to educate landowners about the liability protections that are

provided to them for allowing recreation and educating hunters about proper hunting

behaviors such as avoiding damaging property and other behaviors that can lead to

limited hunting access. Currently, many states provide education to hunters and

landowners through the hunting regulations handbook and their website, but these

resources may be overlooked or individuals may not know they exist.

24

Figure 1.1. Southeast Minnesota landowner survey study area.

25

Table 1.1. Results of Principal Components Analysis. Grouped attitudinal questions

based on factor loading values.

Attitudinal Questions Hunter

Concerns

Deer

Population

Control

Hunting

Tradition

Individual

Hunter

Behavior

Personal

Responsibility

to Manage

Population

Hunter Concerns 1 0.76




Population Control 1 0.79



Hunting Tradition 1 0.76

Hunting Tradition 2 0.74

Hunter Behavior 1 0.61




Personal Responsibility 1 0.83



Personal Responsibility 4 0.79 Extraction Method: Principal Component Analysis

Rotation Method: Varimax with Kaiser Normalization

(Loadings of <0.60 have been suppressed.)

26

Table 1.2. Cronbach’s alpha results of attitudinal variable groups to remove variables

that reduce internal consistency. Only groups with 3 or more variables are analyzed.

Attitudinal Questions

Reliability if

item dropped

Corrected item-

total correlations

Hunter Concerns (α=0.74)

Hunter Concerns 1 0.62 0.74




Deer Population Control (α=0.72)

Population Control 1 0.63 0.63



Individual Hunter Behavior (α=0.80)

Hunter Behavior 1 0.80 0.61




Personal Responsibility to Manage

Population (α=0.83)

Personal Responsibility 1 0.76 0.79




27

Table 1.3. Description of all variables included in analysis.

Variable Description

Access

(Dependent Variable)

Whether or not public hunters were allowed hunting access to

property.

Hunting Beliefs

Management Knowledge Landowner’s knowledge about deer management in southeast

Minnesota.

Hunter Density Total density of hunters on property, excluding public hunters.

Hunter Whether or not landowner hunts.

Hunting Tradition

1) Hunting is a tradition in my family.

2) Allowing other hunters on my property will reduce my or my

family’s opportunity to hunt deer.

Personal Responsibility

1) It is my personal responsibility to manage deer populations.

2) Landowners in my community should be responsible for

managing deer populations.

3) It is my personal responsibility to talk to others in my

community about deer management.

4) Landowners in my community should talk to each other about

managing deer populations.

Deer Population

Perceived Population Deer population on property and surrounding area.

Population Control

1) Hunting reduces damage caused by deer on property.

2) Hunting improves the quality of habitat on property.

3) Hunting on property will help keep deer from being over-

abundant in the area.

Access-related Concerns

DNR Pay Minnesota DNR would pay landowner to allow others to hunt.

Hunter Pay Hunters or an outfitter would pay landowner in order to hunt.

Hunter Behavior

1) Hunters would help landowner out by working on the property.

2) Landowner felt like hunter was interested in getting to know

them and understanding what they’re trying to do on their

property.

3) Landowner knew hunter was safe and ethical.

4) Hunters follow the rules landowner has for hunting on

property.

Hunter Concerns

1) Hunters cause too many problems.

2) Concerned about liability of other hunters on landowner’s

property.

3) Hunting reduces landowner’s privacy.

4) Hunting puts landowner’s livestock at risk.

Land Characteristics

Agricultural parcel Percentage of agricultural land to property owned/leased

Forest parcel Percentage of forest to property owned/leased

Private residence Portion of property is used as private residence.

Distance to city Distance from property to nearest city (meters).

Parcel size Size of property owned based on strata.

Property posted Property is posted.

28

Table 1.4. Posting by private landowners in southeast Minnesota by parcel size. Posting rate of properties and main reason for

posting. Strata: small = 40-79 acres owned, medium = 80-250 acres owned, large >250 acres owned. Chi-squared (χ2) significance test

for comparison between parcel sizes and Cramer’s V for effect size.

Small Medium Large Overall

Characteristics n %1 n %1 n %1 n %1 χ2 Cramer’s V

N 709 818 706 2,233

Posted 333 47 309 38 255 36 897 40 20.40*** 0.10

Posted due to single event? 112 35 116 40 101 43 329 39 0.82 0.02

If yes, why? 100 106 88 294

Control who uses my land 25 25 20 19 20 23 65 22 0.77 0.08

Human safety 8 8 4 4 3 3 15 5 2.80 0.31

Liability concerns 5 5 3 3 4 5 12 4 0.50 0.14

Eliminate trespass 35 35 48 45 26 30 109 37 6.73* 0.18

Keep wildlife for

myself/family/friends 4 4 4 4 3 3 11 4 0.18 0.09

Reduce property damage 1 1 8 8 4 5 13 4 5.69 0.47

Livestock safety 1 1 2 2 5 6 8 3 3.25 0.45

Relationship with neighbor 7 7 7 7 5 6 19 6 0.42 0.11

Better control of deer

population 1 1 2 2 2 2 5 2 0.40 0.20

Family tradition 1 1 0 0 2 2 3 1 2.00 0.58

Conflict with other

recreational users 7 7 3 3 5 6 15 5 1.60 0.23

Other 5 5 5 5 9 10 19 6 1.68 0.21 1Percentages may not total 100% due to rounding.

*p<0.05, ** p<0.01, *** p<0.001

29

Table 1.5. Percentage of private landowners in southeast Minnesota that allowed hunting

access. Separated by parcel size and type of hunters allowed. Strata: small = 40-79 acres

owned, medium = 80-250 acres owned, large >250 acres owned. Chi-squared (χ2)

significance test for comparison between parcel sizes and Cramer’s V for effect size.

Hunter type Small Medium Large Overall χ2 (df=2) Cramer’s V

Overall hunting access 83.9% 87.1% 94.5% 88.4% 40.15*** 0.14

Myself or family 61.8% 62.1% 69.2% 64.2% 10.79** 0.07

Friends or neighbors 59.3% 64.4% 73.7% 65.7% 32.52*** 0.12

Public hunters 9.6% 16.6% 27.2% 17.7% 74.58*** 0.18

People affiliated with

organized hunting group 0.6% 1.7% 2.5% 1.6% 8.01* 0.06

People who lease property 3.3% 4.2% 7.2% 4.9% 12.76** 0.08

Other 1.0% 2.5% 2.8% 2.1% 6.04* 0.05 *p<0.05, ** p<0.01, *** p<0.001 (n=2,194)

30

Table 1.6. Mean number of hunters provided hunting access by landowners that allowed

hunting in southeast Minnesota. Density of hunters is separated by parcel size and type of

hunters allowed. Strata: small = 40-79 acres owned, medium = 80-250 acres owned, large

>250 acres owned. One-way ANOVA to produce F-value and eta-squared (η2) for effect

size. Pairwise T-test for pairwise comparison between parcel sizes with Bonferroni

correction for multiple tests.

Hunter type Small Medium Large Overall F (df=1) η2

Total hunters 5.25ab 6.93ac 9.16bc 7.14 153.70*** 0.08

Myself or family 2.45a 2.57b 3.22ab 2.75 20.04*** 0.01

Friends or neighbors 2.43ab 3.55bc 4.58ac 3.54 88.42*** 0.04

Public hunters 0.18ab 0.48ac 0.86bc 0.51 60.13*** 0.03

People affiliated with organized

hunting group 0.05 0.11 0.13 0.10 1.87 0.00

People who lease property 0.13 0.15 0.21 0.16 2.03 0.00

Other 0.02 0.05 0.16 0.08 9.10** 0.00 Note. Means in a row sharing subscripts are significantly different from each other.

*p<0.05, ** p<0.01, *** p<0.001 (n=1,879)

31

Table 1.7. Results of Pearson’s r bivariate correlation and logit model representing public

hunting access to private lands in southeast Minnesota. Logit coefficients provided by

logit model with lowest AIC value. Odds ratio represents the effect size of each variable.

Characteristics Bivariate Correlation Logit Coefficient Odds Ratio

Land characteristics

Property posted -0.19*** -0.67*** 0.51

Forested parcel -0.16*** -0.17 1.19

Agricultural parcel 0.18*** 0.81** 2.25

Parcel size 0.20*** 0.58*** 1.78

Access-related concerns

Hunter Concerns -0.07** -0.10* 0.91

Hunter Behavior 0.10*** 0.22*** 1.25

DNR Pays 0.08*** 0.02 1.02

Hunter-related factors

Hunter -0.14*** -0.16 0.85

Management Knowledge 0.11*** 0.17* 1.19

Hunting Tradition -0.20*** -0.19*** 0.83

Personal Responsibility -0.12*** -0.09 0.91

Intercept -2.40***

-2 log likelihood -696.57

ΔAIC -1.19 *p<.05, ** p<.01, *** p<.001 (n=1,690)

32

Table 1.8. Results of Pearson’s r bivariate correlation and logit model representing

posting of private lands in southeast Minnesota. Logit coefficients provided by logit

model with lowest AIC value. Odds ratio represents the effect size of each variable.

Characteristics Bivariate Correlation Logit Coefficient Odds Ratio

Access-related concerns

Hunter concerns 0.18*** 0.33*** 1.39

DNR Pays -0.10*** -0.09** 0.91

Hunter-related factors

Hunter 0.17*** 0.49*** 0.20

Management Knowledge -0.16*** -0.32*** 1.63

Hunting Tradition 0.17*** 0.05 1.05

Personal Responsibility 0.15*** 0.11** 1.12

Hunter Density 0.08** 2.27* 9.65

Deer Population

Perceived Population 0.14*** 0.09 1.10

Population Control -0.11*** -0.16*** 0.85

Intercept -1.63***

-2 log likelihood -1,053.63

ΔAIC -15.93 *p<.05, ** p<.01, *** p<.001 (n=1,690)

33

CHAPTER 2

Capability of cell-by-cell correction to reduce mixed-mode sampling effects for

hunter surveys

ABSTRACT Stakeholder surveys are an important source of information for natural

resource managers. Single survey mode designs present the potential for nonresponse

errors and may result in an inaccurate representation of the survey results. Mixed-mode

survey designs are often used to produce more representative results, although they

introduce the potential for measurement error due to mode effects. A cell-by-cell

correction can be applied to survey results to adjust for nonresponse error. We surveyed a

random sample of 2015 Minnesota deer hunters using a sequential mixed-mode design

with Internet and mail surveys. Applying a cell-by-cell correction caused Internet survey

mode results to be significantly different from the combined mixed-mode results and also

inflated variance values. There were significant demographic differences between modes

for age and residence, and between mailing waves for age. Our results also showed that

the fourth mailing wave using a mail survey produced a low response rate and

contributed little to the results. Future surveys need to consider the demographic

composition of their population of interest when choosing survey methodology and

mixed-mode surveys may be a beneficial way to produce representative results.

KEY WORDS: Cell-by-cell correction, deer hunters, demographic differences, mixed-

mode survey, mode effects

34

INTRODUCTION

Many state management agencies use data collected from stakeholder surveys to

assist natural resource management decisions. Survey research, in fact, is a fundamental

method of the applied social sciences, including the human dimensions of wildlife

management (Vaske 2008; Dillman et al. 2014). When confronted with management

issues that involve socioeconomic and ecological challenges, state and federal natural

resources agencies frequently assess public attitudes through surveys to determine user

preferences for various options under consideration (Vaske 2008; Connelly et al. 2012).

Data need to be representative of the population of interest to managers and decision

makers; otherwise, use of the results can lead to incorrect conclusions regarding the

programs and services that managers provide (Vaske 2008; Dillman et al. 2014).

While statistically representative surveys have informed management for almost a

century, the 1990s through the present have been described as “turbulent times” for

survey methodology due to fast-paced cultural and technological changes (Dillman et al.

2009). During the past 20 years, concerns related to unlisted telephone numbers, caller

identification, the transition from landlines to cell phones, the ascent of internet

communications, increasing mail survey expenses, and declining response rates for all

survey modes have led to a shift from single-mode surveys to using multiple modes for

data collection within the same study (Dillman et al. 2014). The expectation is that survey

methods can be consolidated through a mixed-mode strategy to receive the benefits and

reduce the weaknesses from each method; however, this approach presents its own

challenges.

35

In an effort to learn if mixed-mode approaches might better serve its information

needs, the Minnesota Department of Natural Resources (DNR) used a web-first mixed-

mode probabilistic sample survey method that included a combination of Internet and

mail surveys (Smyth et al. 2010; Messer and Dillman 2011; Miller and Dillman 2011).

We believe such a design held potential to reduce costs, improve timeliness, and reduce

nonresponse error (Dillman et al. 2014). This web-first, mixed-mode survey (web+mail)

targeted a sample of white-tailed deer (Odocoileus virginianus) hunters and private

landowners in Minnesota to gather information about their preferences for deer

population levels and management.

Recent studies have suggested that offering a mail questionnaire follow-up to an

initial Internet questionnaire can increase response rates substantially (Smyth et al. 2010;

Messer and Dillman, 2011; Millar and Dillman 2011). Internet surveys are often not

representative of the population if responses are not weighted (Graefe et al. 2011); thus,

they can lead to incorrect management decisions (Gigliotti and Dietsch 2014). When

there is a substantive difference between respondents and nonrespondents, weighting

responses by population proportions can provide a more accurate reflection of the

population (Kalton and Flores-Cervantes 2003). Our goal was to examine whether using

a 2 wave each web + mail sequential mixed-mode survey optimized survey design. We

evaluated whether data collected through different response modes were comparable, and

if not, whether weighting could reduce mode effects. We examined the demographic

differences between response mode and whether the removal of a mail survey mode or 4th

mailing wave would have provided similar results.

36

The specific research questions addressed were: 1) how did the response rates of

different modes and waves of survey administration compare; 2) what differences existed

on key data across modes and waves of data collection; 3) did cell-by-cell correction

reduce sampling effects associated with the use of multiple survey modes; and 4) what

survey mode and administration protocols best balanced efficiency of time and expense

and minimize error.

Conceptual Background

Sources of Error in Surveys

Study designs incorporating a probability sample allow results to be generalized

to the larger target population, allow for the estimation of sampling error, and are more

representative than other types of samples because biases can be avoided (Vaskeet al.

2011). Survey errors are the difference between an estimate from survey data and the true

value in the study population (Dillman et al. 2014). There are four main error sources that

need to be minimized to improve survey estimates, including: coverage error,

measurement error, nonresponse error, and sampling error (Dillman et al. 2014).

Coverage errors occur when there is a difference between the target population and the

study sample (Vaske 2008). For a study to be unbiased, every member of the population

needs to have a known nonzero probability of being sampled (Duda and Nobile 2010;

Dillman et al. 2014).

Measurement error refers to the difference between survey estimates and the true

value caused by respondents providing inaccurate answers to the survey questions

(Dillman et al. 2014). Measurement error can occur because survey questions may not be

understood in the same way by all respondents (Vaske 2008). Nonresponse error occurs

37

when respondents to the survey and nonrespondents would provide responses that differ

in such a way that influences the estimate (Dillman et al. 2014). Nonresponse error can

be countered through encouraging high response rates, conducting nonresponse bias

checks, and weighting data (Vaske 2008). Sampling error refers to the difference between

an estimate based on a sample of the population compared to conducting a census of the

whole population (Dillman et al. 2014). Large sample sizes are usually adventageous to

minimize sampling error (Vaske 2008). Sample data may be representative of and

generalizable to the target sample population if a large sample size is used, while

minimizing potential sources of error related to coverage, nonresponse, and measurement

errors (Vaske 2008).

Survey Modes

Probabilistic Internet surveys typically have considerably lower response rates

than mail surveys, although this difference varies within populations (Shih and Fan 2009;

Borkan 2010; Smyth et al. 2010; Graefe et al. 2011; Shin et al. 2012; Schouten et al.

2013). Internet surveys are attractive due to the cost savings associated with eliminating

printing, mailing, and data entry costs, while providing prompt responses (Vaske 2008;

Lesser et al. 2011; Connelly et al. 2012; Dillman et al. 2014). Internet surveys have also

produced more accurate results with lower item non-response than other survey modes

(Shin et al. 2012). Internet surveys typically suffer from undercoverage of the population

due to incomplete contact information, while mail surveys are assumed to have full

coverage (Schouten et al. 2013). Given the allure of Internet surveys coupled with their

short-comings, mixed-mode surveys have become much more common in survey

research.

38

Mixed-mode surveys are distributed using multiple methods of contact (e.g., mail,

Internet, phone, face-to-face) and allow researchers to offer participants several response

mechanisms from which they can choose to complete the survey (Carrozzino-Lyon et al.

2013). Mixed-mode designs provide the benefits of each survey mode while minimizing

weaknesses at an affordable cost, increasing the perceived importance of the survey for

participants, and decreasing perceived personal cost of participation (de Leeuw 2005;

Sexton et al. 2011; Carrozzino-Lyon et al. 2013). Mixed-mode surveys can reduce

coverage error by contacting different groups of hard-to-reach respondents and increasing

response rates (Dillman et al. 2009; Vannieuwenhuyze et al. 2010; Messer and Dillman

2011). Demographic groups have different levels of nonresponse to survey modes,

making mixed-mode surveys advantageous because nonrespondents can be approached

subsequently with a different mode to increase response rates (Voogt and Saris 2005;

Vannieuwenhuyze et al. 2010). A mail survey is a potentially appropriate supplementary

survey mode in addition to Internet surveys because both modes use many of the same

communication channels (i.e., visual; Dillman et al. 2009), although both modes need to

be carefully designed to closely resemble each other to assure the validity and reliability

of the survey (Smyth et al. 2010; Carrozzino-Lyon et al. 2013).

Weighting Strategies

One of the key concerns in a mixed-mode approach is the comparability of data

collected across different survey modes for the same study population (Vooget and Saris

2005; Vannieuwenhuyze et al. 2010; Dillman et al. 2014). The ability to compare data

between survey modes is limited because the nature and magnitude of coverage,

nonresponse, and measurement errors differ across modes (Jackle et al. 2010; Schouten et

39

al. 2013). Mode effects are the combined differences between survey modes caused by

sampling errors, coverage errors, selective nonresponse, and measurement bias (Schouten

et al. 2013). There are three approaches available to minimize mode effects: good

questionnaire design, adapting the choice of survey modes to the population of interest,

and using weighting or matching techniques after data collection (Schouten et al. 2013).

We focus on weighting techniques since they can be applied after data collection to

ensure that respondents represent the target population.

Respondents may not be representative of the target population if the individuals

who completed the questionnaire are different from nonrespondents (Vaske et al. 2011).

Weighting can be used when population proportions are known in advance and survey

results reveal that specific groups are overrepresented or underrepresented in the data

(Vaske et al. 2011). Weighting adjustments are mainly used to reduce bias in the survey

estimates caused by nonresponse and noncoverage error with the aim of making the

weighted sample more reflective of the larger population (Kalton and Flores-Cervantes

2003; Vaske et al. 2011). Auxiliary information can be used to compensate for

nonresponse by identifying respondents who are similar to nonrespondents, and then

applying weights to respondents so that they represent similar nonrespondents (Kalton

and Flores-Cervantes 2003). Large variability of weighted adjustments can cause inflated

variances of survey estimates and lower precision of the survey estimates, particularly

when small sample sizes occur in a number of the adjustment cells (Kalton and Flores-

Cervantes 2003; Vaske et al. 2011).

Although there are several weighting methods that can be used, we focused on

weighting based on population proportions through cell-by-cell correction. In many cases

40

little is known about nonrespondents other than strata or demographic information, so a

simple cell weighting adjustment might be a practical weighting strategy (Kalton and

Flores-Cervantes 2003). Population proportions from a single variable can be used to

perform a cell-by-cell correction to adjust for overrepresented or underrepresented groups

(Vaske et al. 2011). The correction weight that a cell receives is calculated by taking the

corresponding cell proportion in the sample population divided by the cell proportion

among survey responses. This simple weighting scheme allows responses within each

cell to become more representative of the sample population.

Weight =

For example, if males represent 80% of the population but only represent 40% of

respondents, then males are underrepresented and would receive a 2.0 correction weight.

Multiple variable cell-by-cell weighting can be used if population proportions are known

in advance and specific groups are found to be overrepresented or underrepresented

within survey results (Vaske et al. 2011). Variables such as age, gender, and residence

can be used to create more cells for comparison. The combination of three variables and

their respective levels can result in 12 cells (3 age classes * 2 genders * 2 residence

classes). Cell weighting adjustments for nonresponse assumes that the respondents within

a cell represent the nonrespondents within that cell, but unlike other methods, makes no

assumptions about the structure of the response probabilities across cells (Kalton and

Flores-Cervantes 2003).

41

METHODS

Study Context and Survey Methods

We examined the results of a survey of white-tailed deer hunters in northwest

Minnesota. The goal of the survey was to collect information about public desires and

opinions regarding deer management in northwest Minnesota. Hunters were surveyed

using a sequential web + mail, or web-push, mixed-mode design (Dillman et al. 2014)

that included two waves of letters through the mail requesting recipients complete a

Qualtrics Internet survey, followed by two waves that include a self-administered mail

back questionnaire. Deer hunters in northwest Minnesota were the population of interest.

We randomly sampled 7,801 adults who had purchased a license for the 2014 Minnesota

deer hunting season and indicated they intended to hunt within a deer permit area (DPA)

in the study area. Approval for human subjects research received via University of

Minnesota IRB Study Number: 1405E50825. Survey recipients were contacted four times

between February and May, 2015 with data collected until July, 2015.

Data Analysis

Data analysis was conducted using Program R (R Version 3.2.2, www.r-

project.org, accessed 24 November 2015). Survey mode and wave were examined to

determine if there were significant demographic differences. T-tests were used to

examine demographic differences among respondents based on survey mode. ANOVA

tests were used to examine if demographic differences existed among respondents based

on mailing wave and t-tests were used to examine demographic differences between

waves within each survey mode (i.e., wave 1 vs 2, wave 3 vs 4).

42

Data analysis methods were based on those used by Vaske et al. (2011) to

compensate for sampling issues in Internet surveys through weighting. We weighted

survey responses to adjust for overrepresented or underrepresented groups based on

multiple variable proportions within our sample. Three weighting variables were used: 1)

age (<35, 35-55, >55 years), 2) gender (male, female), and 3) residence (urban, rural).

Age will hereafter be referred to as: young (<35 years old), middle-aged (35-55 years

old), and older (>55 years old). The combination of the three variables and their

respective levels resulted in 12 cells (3 * 2 * 2). Residence classification was based on

the population size of the city where sampled individuals had their permanent residence.

Cities with at least 2,500 people based on the 2010 United States census were considered

urban (U.S. Census Bureau 2015). Age, gender, and residence were chosen because these

demographic data were available through the Minnesota DNR license database and these

variables have been found to be related to response propensity and to be influential for

survey weighting (Tivesten et al. 2012).

Survey mode (Internet or mail) and mailing wave were used to group responses

into three response mode-groups: 1) responses to the two Internet survey waves

(Internet), 2) responses to the two Internet survey waves and the first mail survey wave

(3-wave), and 3) responses to the two Internet survey waves and the two mail survey

waves (Combined). Each mode-group received a unique cell-by-cell correction based on

the number of responses within each weighting cell compared to the expected number of

responses based on sample proportions. Chi-squared tests were used to determine if the

observed number of responses within a cell differed significantly from the expected

number of responses. Responses within a cell received a correction if the p-value from

43

the chi-squared test was significant after applying a Bonferroni correction (p-

value/number of tests) and the cell had more than 10 responses. The Bonferroni

correction was applied to adjust for multiple tests being conducted to reduce the potential

for Type 1 error. Cells with fewer than 10 responses did not receive a correction to avoid

variance inflation.

Responses to five survey questions, measuring respondent’s attitudes on key

issues related to deer management, were selected for evaluation. Mean values for each

question were calculated and then compared between the uncorrected control mode-group

(Combined Uncorrected) and the other two uncorrected mode-groups. Corrected mode-

groups were compared in the same manner as the uncorrected mode-groups. T-test

comparison between mode-groups was used to determine if removing a survey mode or

wave would produce results that did not differ significantly from the control mode-group.

Additionally, t-tests were used to determine if applying a cell-by-cell correction to

responses for each survey question resulted in significant differences between the

corrected control mode-group to each of the correction mode-groups.

RESULTS

Of the 7,801 surveys mailed, 332 were undeliverable and 3,095 were returned,

yielding an adjusted response rate of 41.4% (Table 2.1). A total of 1,934 individuals

responded to the Internet survey (25.9%) and 1,161 responded to the mail survey

(15.5%). Of these, 840 responded to the first wave (11.2% response rate), 1,094

responded to the second wave (14.6%), 883 responded to the third wave (11.8%), and

278 responded to the fourth wave (3.7%). Removal of the 4th mailing wave (37.7%; 3-

wave mode-group) resulted in a slightly lower response rate than the Combined mode-

44

group (41.4%) and produced similar response rates for each age, gender, and residence

category. Middle-aged and older recipients had a higher response rate than younger

recipients across all mailing waves and mode-groups. Males had a higher response rate

across all mailing waves and mode-groups compared to female recipients. Also, urban

residents had a higher response rate to the Internet survey compared to rural residents

who responded to the mail survey at a slightly higher rate.

We examined the demographic differences between survey mode and mailing

waves. We found that respondents to the mail survey were significantly older (M=54.1)

than Internet respondents (M=47.4, t=-11.23, p<0.001, η2=0.042). Males were slightly

more likely to use the mail survey (87.6%) than the Internet survey (86.8%) but the

difference was not significant. Urban respondents were significantly more likely to

respond to the Internet survey (57.5%) than the mail survey (47.4%, t=5.48, p<0.001,

η2=0.010). We also found significant differences in Internet access between urban and

rural respondents (94% vs 89%; p<0.001) and females and males (94% vs 91%; p<0.05).

Internet access was significantly different by age class with younger individuals (99%)

having greater access than middle-aged (97%) or older individuals (83%, p<0.001).

We found significant differences between mailing waves for age (F=49.8,

p<0.001) and residence (F=18.9, p<0.001), but was not significant for gender (Table 2.2).

Respondents were significantly older in wave 1 (M=48.7) than wave 2 (M=46.4, t=3.47,

p<0.001), and wave 3 (M=55.5) than wave 4 (M=49.6, t=5.12, p<0.001). There was not a

significant difference in gender between respondents to wave 1 (88.3%) compared to

wave 2 (85.6%), and wave 3 (87.8%) compared to wave 4 (87.1%). There was not a

45

significant difference in urban residence between respondents to wave 1 (56.4%) than

wave 2 (58.3%), and wave 3 (47.2%) than wave 4 (47.8%).

We partitioned respondents into 12 cells by age, gender, and residence and found

a significant difference between the number of observed and expected responses within

each cell (χ2=113.2, p<0.001) indicating a cell-by-cell correction was needed for the

Internet mode-group (Table 2.3). After applying the Bonferroni correction (p<0.004) four

cells did not represent the sample population. Young rural respondents were

underrepresented and urban males were overrepresented within the Internet mode-group.

When examining responses from the first three mailing waves, we found a significant

difference (χ2=249.8, p<0.001) between the expected and observed number of responses

per cell and a cell-by-cell correction was necessary; six cells were misrepresentative of

the sample population – young adults of both sexes were underrepresented, while older

males were overrepresented (Table 2.4). When examining the combined responses from

the Internet and mail survey responses, we found a significant difference (χ2=262.3,

p<0.001) between the expected and observed number of responses per cell and a cell-by-

cell correction was necessary; six cells misrepresented the sample population – young

adults of both sexes were underrepresented compared to the sample population, while

older males were overrepresented within the Combined mode-group (Table 2.5). The

same cells were underrepresented and overrepresented in the 3-wave mode-group as the

Combined mode-group.

We examined the mean values of responses to five questions based on corrected

and uncorrected responses in three mode-groups (Tables 2.6 and 2.7). Mean values for

the uncorrected Internet and 3-wave mode-groups were not significantly different from

46

the Combined Uncorrected values (Table 2.7). The variance for each of the uncorrected

mode-groups did not vary by a large amount from the Combined Uncorrected variance

values. Comparing the corrected control mode-group (Combined Corrected) and the other

two corrected mode-groups allowed us to examine the impacts of applying a cell-by-cell

correction on variance and mean values. The Internet Corrected responses produced

significantly lower mean values for each question and the lowest variance values among

corrected mode-groups. Variance values for all corrected mode-groups were larger than

the uncorrected mode-groups. The 3-wave Corrected responses were not significantly

different from the Combined Corrected mode-group. The Combined Uncorrected mean

values were lower than the Combined Corrected, but the difference was not significant.

DISCUSSION

A majority of respondents used the Internet survey mode (25.9%) though the

addition of a mail survey (15.5%) improved the overall response rate. The removal of the

4th mailing wave (37.7%; 3-wave mode-group) reduced the overall response rate slightly

and produced similar response rates for each age, gender, and residence category.

Responses to each mailing wave and survey mode showed a response bias in age,

urban/rural residence, and gender when compared to the study population. Respondents

to the Internet survey were significantly younger and more urban than mail survey

respondents. There were significant differences between mailing waves for age and

residence, but not for gender. Gender was not expected to be significant due a large

majority of the sample population being male, limiting the potential for a gender response

bias. Past studies have shown similar age differences between survey modes (Smyth et al.

2010; Graefe et al. 2011; Carrozzino-Lyon et al. 2013).

47

With the current study design we were unable to determine if any differences

between mode-groups is due to survey mode or demographic causes. Mode effects are

difficult to correct for because they are often confounded with self-selection effects (de

Leeuw 2005). Answers may differ between survey modes because of the survey mode or

because different subgroups responded in different modes (de Leeuw 2005). These results

illustrate the importance of carefully considering demographic characteristics of the study

population and choosing survey modes accordingly.

Within our study, there was a significant difference in Internet access between

urban and rural respondents (94% vs 89%; p<0.001) and females and males (94% vs

91%; p<0.05) though these differences in Internet access were likely too small to impact

response rates. Internet access varied to a greater degree by age class with younger

individuals (99%) having significantly greater access than middle-aged (97%) or older

individuals (83%, p<0.001). Internet access has been found to be more limited in rural

than urban areas and among older Americans, those with lower incomes, and those with

less education (Smyth et al. 2010). Older individuals are less likely to complete Internet

surveys due in part to lower Internet-access rates (Smyth et al. 2010).

In addition to careful consideration of survey mode, our results demonstrate the

importance of using a weighting strategy (e.g., cell-by-cell weighting) to compensate for

mode effects. Because all three response mode-groups misrepresented the sample

population, a cell-by-cell correction was applied to make the responses more

representative. Across all correction mode-groups young individuals were

underrepresented and older individuals were overrepresented. These findings match past

48

studies that found younger adults to have lower response rates to the Internet and mail

portions of a mixed-mode survey (Dillman et al. 2009; Gigliotti and Dietsch 2014).

Before applying the cell-by-cell correction, the three mode-groups produced

similar mean values for the five survey questions, while the corrected responses showed

greater differences between mode-groups and larger variance values. The Internet

Corrected mean values were significantly lower than the Combined Corrected mean

values for each question, while the 3-wave Corrected mean values did not vary

significantly. The Combined Corrected and 3-wave mode-groups produced similar

variance values, while the Internet Corrected mode-group produced lower variance

values. Due to our sample size we did not have an issue with low number of responses

within correction cells that would cause inflated variance values. This may be a problem

in other studies where some groups may experience lower response rates caused by

survey mode or smaller sample size within the survey population (e.g., female hunters).

The 3-wave results were likely similar to the Combined responses due to the low

response rate in the 4th mailing wave (3.7% response rate).

Our results suggest that using an Internet survey without a mail survey would not

provide representative results of the study population due to significant demographic

differences. We were unable to determine the impacts of using the mail survey in a

single-mode approach since we did not have a true experimental design. Using a single

survey mode would likely result in certain demographic groups being misrepresentative

of the survey population depending on the survey mode selected. Demographic

characteristics have been shown to be strongly aligned with survey mode preferences

with rural, older respondents choosing the mail survey mode more often and younger,

49

urban respondents choosing the Internet survey mode more often (Smyth et al. 2010;

Messer and Dillman 2011; Carrozzino-Lyon et al. 2013). Older recipients who use the

Internet will respond to an Internet survey, but a large contingent of older individuals

would prefer not to use the Internet survey (Gigliotti and Dietsch 2014). Respondents to a

single survey mode may not be representative of others in a similar demographic, such as

older Americans participating in an Internet survey may have more education than

nonrespondents.

Since a significant number of respondents still lack Internet access and others

prefer to respond by mail, there is a need for a supplementary mode such as mail along

with an Internet mode (Smyth et al. 2010). A mixed-mode design using an Internet

survey with a mail follow-up increases response rates and provides better representation

of the general public than using an Internet survey alone, especially among individuals

who are less likely to have internet access or who live in rural areas (Lesser et al. 2011;

Smyth et al. 2010). Optimal survey design requires careful consideration of the different

sources of error present and whether expected mode effects are serious enough to avoid

mixed-mode designs compared to the advantages (de Leeuw 2005).

Our results suggest that using a three-wave mixed-mode design instead of a four-

wave design would be advantageous due to the similarity of survey responses, while

providing multiple response modes to account for demographic differences due to survey

mode. Eliminating the fourth mailing wave would be finiancially benefical due to the

printing, mailing, and data entry costs that are associated with a mail survey, while only

experiencing a small loss in response rate. Combining the most cost effective method

with a second, more expensive method provides the benefits of cost savings and less error

50

than a single-mode approach, while providing higher response rates (de Leeuw 2005).

Using a mixed-mode survey design with Internet and mail survey options provides survey

recipients the choice of survey mode and reduces the impact of demographic differences

between survey modes.

A cell-by-cell correction reduced nonresponse errors and sampling effects

associated with the use of multiple survey modes, though this approach can inflate

variance. We did observe significant demographic differences between survey modes for

age and residence, and between mailing waves for age. Our results indicated that we

could have removed results from the 4th mailing wave and still produced valid and

accurate results. A sequential mixed-mode survey design using two Internet survey

mailings and a single paper survey mailing wave along with a cell-by-cell correction can

produce statistically representative results. Using a combination of Internet and paper

surveys is beneficial because it allows for higher response rates than an Internet survey

alone, while accounting for demographic differences present between survey modes and

using the same information transmission mode to avoid some mode effects. Some

individuals had difficulty accessing the Internet survey and/or expressed a preference for

an alternative survey mode. The 4th mailing wave can be removed due to a low response

rate that did not alter the demographic makeup or response means. Internet surveys were

beneficial due to their cost savings and quicker access to data, but required more

educational effort and technical problem solving than a paper survey.

In summary, we found that a sequential web + mail mixed-mode survey design

was a beneficial approach for survey research due to the response rates and demographic

differences within our study. A majority of respondents used the Internet survey (25.9%)

51

though the addition of a paper survey improved response rates (37.7%). There were

significant demographic differences between survey modes and mailing waves

reinforcing the importance of a mixed-mode design to balance demographic response

bias. Cell-by-cell correction was necessary within our survey results because the

demographic proportions of respondents were not representative of the sample

population. A sequential web + mail survey design using three mailing waves was found

to best balance the time and expenses while minimizing sources of error.

Future reseach should examine the impact of completing an Internet survey with a

mobile telephone compared to completion with a computer. Survey practitioners must

consider how mobile phone use will affect the survey response process as smartphone use

increases and more individuals rely on them for internet access (Millar and Dillman

2012; Dillman et al. 2014). Optimizing surveys for mobile completion can be difficult,

especially on lengthy or difficult questionnaires (Toepoel and Lugtig 2014). Mobile

phones provide opportunities because they can use both aural and visual channels of

communication to enrich the survey and collect data such as GPS or motion (Toepoel and

Lugtig 2014).

52

Table 2.1. Adjusted response rate for demographic groups (age, gender, residence) by mailing wave and response mode-group.

1st Wave

(Internet)

2nd Wave

(Internet)

Combined

Internet Only

3rd Wave

(Mail)

Internet +

3rd Wave

4th Wave

(Mail)

Total

Combined

Sample

Size Invalid

% n % n % n % n % n % n % n n n

<35 years old 7.6 182 12.2 291 19.8 473 5.7 135 25.5 608 2.9 70 28.4 678 2,545 161

35-55 years old 12.1 352 16.8 488 28.9 840 8.9 260 37.8 1,100 3.3 95 41.1 1,195 3,029 119

>55 years old 14.1 306 14.5 315 28.6 621 22.4 488 51.0 1,109 5.2 113 56.2 1,222 2,227 52

Male 11.7 742 14.7 936 26.4 1,678 12.2 775 38.6 2,453 3.8 242 42.4 2,695 6,610 253

Female 8.8 98 14.2 158 23.0 256 9.7 108 32.7 364 3.2 36 36.0 400 1,191 79

Urban 12.5 474 16.8 638 29.3 1,112 11.0 417 40.3 1,529 3.5 133 43.8 1,662 3,954 163

Rural 10.0 366 12.4 456 22.3 822 12.7 466 35.0 1,288 3.9 145 39.0 1,433 3,847 169

Overall 11.2 840 14.6 1,094 25.9 1,934 11.8 883 37.7 2,817 3.7 278 41.4 3,095 7,801 332 Combined Internet Only = Responses to first two mailing waves, two Internet survey mailings.

Internet + 3rd Wave = Responses to first three mailing waves, two Internet survey mailings and a mail survey mailing.

Total Combined = Responses to four mailing waves, two Internet survey mailings and two mail survey mailings.

53

Table 2.2. Comparison of demographic variables by mailing wave. Used T-tests to compare waves within survey modes (Wave 1 vs 2

& Wave 3 vs 4) and a one-way ANOVA to test for significant differences between all four mailing waves. Calculated eta squared (η2)

to determine effect size of the demographic variable.

Wave T-test

(Wave 1 vs 2)

T-test

(Wave 3 vs 4)

One-way

ANOVA

1 2 3 4 t p-value t p-value F η2

Mean Age 48.7 46.4 55.5 49.6 3.47 <0.001 5.12 <0.001 49.81*** 0.016

Gender

(% Male) 88.3% 85.6% 87.8% 87.1% 1.81 0.071 0.31 0.755 0.07 0.000

Residence

(% Urban) 56.4% 58.3% 47.2% 47.8% -0.83 0.405 -0.18 0.858 18.88*** 0.006

*p<.05, ** p<.01, *** p<.001 (n=3,095)

54

Table 2.3. Cell-by-cell correction for responses to first two survey mailings, two Internet survey mailings (Internet mode-group).

Survey recipients are partitioned into 12 cells by demographic variables (age, gender, residence). Observed and expected number of

responses are compared through Chi-squared (χ2) analysis to determine significant response differences.

Gender Residence Age

Sample

size

n

Sample

size

%

Observed

responses

n

Observed

responses

%

Expected

responses1

Chi-squared

test statistic

Difference

test p-value

Statistical

significance

Bonferroni

correction2

Male Rural <35 913 11.7 157 8.1 226.3 23.72 <0.001 Yes

35-55 1,191 15.3 283 14.6 295.3 0.55 0.457 No

>55 1,051 13.5 236 12.2 260.6 2.57 0.109 No

Urban <35 1,112 14.3 240 12.4 275.7 5.24 0.022 No

35-55 1,396 17.9 451 23.3 346.1 38.36 <0.001 Yes

>55 947 12.1 311 16.1 234.8 27.80 <0.001 Yes

Female Rural <35 289 3.7 37 1.9 71.6 16.90 <0.001 Yes

35-55 249 3.2 59 3.1 61.7 0.08 0.773 No

>55 154 2.0 50 2.6 38.2 3.42 0.064 No

Urban <35 231 3.0 39 2.0 57.3 5.68 0.017 No

35-55 193 2.5 47 2.4 47.8 0.00 0.959 No

>55 75 1.0 24 1.2 18.6 1.31 0.253 No

Total 7,801 100% 1,934 χ2 = 113.19,

p<0.001

1Expected response = total sample size * population percent 2Bonferroni correction = p-value/number of tests = 0.05/12 = 0.004

55

Table 2.4. Cell-by-cell correction for responses to first three survey mailings, two Internet and one mail survey mailings (3-wave

mode-group). Survey recipients are partitioned into 12 cells by demographic variables (age, gender, residence). Observed and

expected number of responses are compared through Chi-squared (χ2) analysis to determine significant response differences.


Sample

size

n

Sample

size

%

Observed

responses

n

Observed

responses

%

Expected

responses1

Chi-squared

test statistic

Difference

test p-value

Statistical

significance

Bonferroni

correction2

Male Rural <35 913 11.7 208 7.4 329.7 50.45 <0.001 Yes

35-55 1,191 15.3 403 14.3 430.1 1.94 0.164 No

>55 1,051 13.5 471 16.7 379.5 25.20 <0.001 Yes

Urban <35 1,112 14.3 291 10.3 401.6 35.18 <0.001 Yes

35-55 1,396 17.9 557 19.8 504.1 6.63 0.010 No

>55 947 12.1 523 18.6 342.0 108.47 <0.001 Yes

Female Rural <35 289 3.7 57 2.0 104.4 21.85 <0.001 Yes

35-55 249 3.2 74 2.6 89.9 2.73 0.098 No

>55 154 2.0 75 2.7 55.6 6.55 0.011 No

Urban <35 231 3.0 52 1.8 83.4 11.81 <0.001 Yes

35-55 193 2.5 66 2.3 69.7 0.15 0.699 No

>55 75 1.0 40 1.4 27.1 5.75 0.017 No

Total 7,801 100% 2,817 χ2 = 249.75,

p<0.001


56

Table 2.5. Cell-by-cell correction for responses to all four survey mailings, two Internet and two mail survey mailings (Combined

mode-group). Survey recipients are partitioned into 12 cells by demographic variables (age, gender, residence). Observed and

expected number of responses are compared through Chi-squared (χ2) analysis to determine significant response differences.


Sample

size

n

Sample

size

%

Observed

responses

n

Observed

responses

%

Expected

responses1

Chi-squared

test statistic

Difference

test p-value

Statistical

significance

Bonferroni

correction2

Male Rural <35 913 11.7 234 7.6 362.2 51.01 <0.001 Yes

35-55 1,191 15.3 441 14.2 472.5 2.40 0.121 No

>55 1,051 13.5 531 17.2 417.0 35.72 <0.001 Yes

Urban <35 1,112 14.3 320 10.3 441.2 38.50 <0.001 Yes

35-55 1,396 17.9 599 19.4 553.9 4.38 0.036 No

>55 947 12.1 570 18.4 375.7 113.76 <0.001 Yes

Female Rural <35 289 3.7 64 2.1 114.7 22.79 <0.001 Yes

35-55 249 3.2 83 2.7 98.8 2.44 0.118 No

>55 154 2.0 80 2.6 61.1 5.65 0.017 No

Urban <35 231 3.0 60 1.9 91.6 10.91 <0.001 Yes

35-55 193 2.5 72 2.3 76.6 0.22 0.638 No

>55 75 1.0 41 1.3 29.8 3.92 0.048 No

Total 7,801 100% 3,095 χ2 = 262.31,

p<0.001


57

Table 2.6. Survey questions used to analyze the impacts of cell-by-cell correction.

Question Description

A1 Over the past 5 years, what trend have you seen in the deer population in the permit area

you hunt most often?

B2 In thinking about the deer permit area you hunt, please indicate your overall satisfaction

with current deer numbers.

C3 In thinking about the property you hunt and the surrounding area, at what level do you

think the deer population should be managed?

D4 To what extent would you support or oppose a regulation that would increase the

proportion of antlered bucks in the deer area you hunt most often?

E5 How satisfied were you with your 2014 deer hunt? 1Scale: 1 = Much fewer deer to 5 = Many more deer.

2Scale: 1 = Very dissatisfied to 5 = Very Satisfied. 3Scale: 1 = Decrease population 50% to 7 = Increase population 50%. 4Scale: 1 = Strongly oppose to 5 = Strongly support. 5Scale: 1 = Very dissatisfied to 5 = Very satisfied.

58

Table 2.7. Mean values (M) for uncorrected and corrected responses to five key management relevant questions. Responses were

grouped into three mode-groups: 1) Internet surveys only (Internet), 2) first three mailing waves (3-wave), and 3) combined Internet

and mail surveys (Combined). Calculated variance (σ2) and T-tests were calculated between the uncorrected or corrected control

mode-group (Combined) and the three other mode-groups that are either uncorrected or corrected, respectively.

Question

A B C D E

Correction M t σ2 M t σ2 M t σ2 M t σ2 M t σ2

Internet

Uncorrected 2.02 -0.61 1.23 2.63 -0.50 2.02 2.29 0.91 0.67 3.60 -1.71 1.45 3.18 -0.55 1.82

3-wave

Uncorrected 2.00 0.04 1.21 2.61 0.00 1.96 2.29 0.68 0.67 3.55 -0.11 1.47 3.16 -0.07 1.82

Combined

Uncorrected 2.00 - 1.21 2.61 - 1.93 2.31 - 0.84 3.54 - 1.45 3.16 - 1.81

Internet

Corrected 1.94 2.37* 1.43 2.53 2.33* 2.33 2.19 4.25*** 0.92 3.47 3.00** 2.20 3.03 2.88** 2.15

3-wave

Corrected 2.04 -0.28 1.75 2.65 -0.37 2.77 2.32 0.18 1.21 3.63 -0.54 3.08 3.18 -0.41 2.71

Combined

Corrected 2.03 - 1.73 2.64 - 2.66 2.33 - 1.61 3.61 - 2.97 3.17 - 2.65

*p<0.05, **p<0.01, ***p<0.001

A. Over the past 5 years, what trend have you seen in the deer population in the permit area you hunt most often?

B. In thinking about the deer permit area you hunt, please indicate your overall satisfaction with the current deer numbers.

C. In thinking about the property you hunt and the surrounding area, at what level do you think the deer population should be managed?

D. To what extent would you support or oppose a regulation that would increase the proportion of antlered bucks in the deer area you hunt most often?

E. How satisfied were you with your 2014 deer hunt?

59

LITERATURE CITED

Benson, D. E. 2001. Wildlife and recreation management on private lands in the United

States. Wildlife Society Bulletin 29:359-371.

Bissonette, J. A., C. A. Kassar, and L. J. Cook. 2008. Assessment of costs associated with

deer-vehicle collisions: human death and injury, vehicle damage, and deer loss.

Human-Wildlife Conflicts 2:17-27.

Borkan, B. 2010. The mode effect in mixed-mode surveys. Social Science Computer

Review 28:371-380.

Brown, T. L., D. J. Decker, and J. W. Kelley. 1984. Access to private lands for hunting in

New York: 1963-1980. Wildlife Society Bulletin 12:344-349.

Brown, T. L., D. J. Decker, S. J. Riley, J. W. Enck, T. B. Lauber, P. D. Curtis, and G. F.

Mattfeld. 2000. The future of hunting as a mechanism to control white-tailed deer

populations. Wildlife Society Bulletin 28:797-807.

Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: a

practical information-theoretic approach. 2nd edition. Springe, New York, NY,

USA.

Carrozzino-Lyon, A., S.L. McMullin, and J.A. Parkhurst. 2013. Mail and web-based

survey administration: a case study with recreational users of Virginia’s wildlife

management areas. Human Dimensions of Wildlife 18:219-233.

Christoffel, R. A., and S. R. Craven. 2000. Attitudes of woodland owners toward white-

tailed deer and herbivory in Wisconsin. Wildlife Society Bulletin 28:227-234.

Connelly, N.A., W.F. Siemer, D.J. Decker, and S.B. Allred. 2012. Methods of human

dimensions inquiry. Chapter 10 in Human Dimensions of Wildlife Management,

edited by D.J. Decker, S.J. Riley and W.F. Siemer, 123-138. The John Hopkins

University Press, Baltimore, MD, USA

Conover, M. R. 2001. Effect of hunting and trapping on wildlife damage. Wildlife

Society Bulletin 29:521-532.

Conover, M. R. 1997. Monetary and intangible valuation of deer in the United States.

Wildlife Society Bulletin 25:298-305.

Cordell, H. K., J. C. Bergstrom, R. J. Teasley, and J. A. Maetzold. 1998. Trends in

outdoor recreation and implications for private land management in the East. In:

Proceedings of the Natural Resources Income Opportunities for Private Lands

Conference, April 5-7, 1998, Hagerstown, MD (pp. 4-10). University of Maryland

Cooperative Extension Service, College Park, MD, USA.

Curtis, P. D., A. N. Moen, J. W. Enck, S. J. Riley, D. J. Decker, and G. F. Mattfield.

2000. Can white-tailed deer be managed at the landscape level using traditional

hunter harvest? Publication 00-4, Human Dimensions Research Unit, Department

of Natural Resources, Cornell University, Ithaca, NY, USA.

60

de Leeuw, E. 2005. To Mix or Not to Mix Data Collection Modes in Surveys. Journal of

Official Statistics 21:233-255.

Decker, D. J., and T. L. Brown. 1979. Hunting in New York: participation, demand, and

land access. New York Fish and Game Journal 26:101-131.

Diefenbach, D. R., J. C. Finley, A. E. Luloff, R. Stedman, C. B. Swope, H. C. Zinn, and

G. J. San Julian. 2005. Bear and deer hunter density and distribution on public

land in Pennsylvania. Human Dimensions of Wildlife 10: 201-212.

Dillman, D. A., J. D. Smyth, and L. M. Christian. 2009. Internet, mail, and mixed-mode

surveys: the tailored design method. 3rd edition. Wiley and Sons, New York, NY,

USA.

—. 2014. Internet, phone, mail, and mixed-mode surveys: the tailored design method.

Fourth edition. Wiley and Sons, Hoboken, NJ, USA.

Duda, M.D., and J.L. Nobile. 2010. The fallacy of online surveys: no data are better than

bad data. Human Dimensions of Wildlife 15:55-64.

ESRI. 2013. ArcGIS Desktop: Release 10.2. Environmental Systems Research Institute,

Redlands, CA, USA.

Fieberg, J., L. Cornicelli, D. C. Fulton, and M. D. Grund. 2010. Design and analysis of

simple choice surveys for natural resource management. Journal of Wildlife

Management 74:871-879.

Fulton, D. C., L. Cornicelli, and M. D. Grund. 2006. 2005 Survey of deer hunter

satisfaction and preferences for regulation changes in Minnesota. Unpublished

Project Report, Minnesota Cooperative Fish and Wildlife Research Unit, St. Paul,

MN, USA.

Gigliotti, L., and A. Dietsch. 2014. Does age matter? The influence of age on response

rates in a mixed-mode survey. Human Dimensions of Wildlife 19:280-287.

Graefe, A., A. Mowen, E. Covelli, and N. Trauntvein. 2011. Recreation participation and

conservation attitudes: differences between mail and online respondents in a

mixed-mode survey. Human Dimensions of Wildlife 16:183-199.

Gramann, J. H., D. E. Albrecht, T. M. Bonnicksen, and W. B. Kurtz. 1985. Recreational

access to private forests: the impact of hobby farming and exclusivity. Journal of

Leisure Research 17:234-240.

Jackle, A., C. Roberts, and P. Lynn. 2010. Assessing the effect of data collection mode

on measurement. International Statistical Review 78:3-20.

Jagnow, C. P., A. E. Luloff, J. C. Finley, and G. San Julian. 2008. Private land access and

deer management in three Pennsylvania counties: key informants’ perspectives.

Human Dimensions of Wildlife 13:102-114.

61

Jagnow, C. P., R. C. Stedman, A. E. Luloff, G. J. San Julian, J. C. Finley, and J. Steele.

2006. Why landowners in Pennsylvania post their property against hunting.

Human Dimensions of Wildlife 11:15-26.

Kalton, G., and I. Flores-Cervantes. 2003. Weighting methods. Journal of Official

Statistics 19:81-97.

Lacey, J. R., K. Jamtgaard, L. Riggle, and T. Hayes. 1993. Impacts of big game on

private land in south-western Montana: landowner perceptions. Journal of Range


Land Management Information Center. 1983. Minnesota Public Lands, 1983. Minnesota

State Planning Agency, St. Paul, MN, USA.

Lauber, T. B., and T. L. Brown. 2000. Hunting access on private lands in Dutchess

County. Publication 001-12, Human Dimensions Research Unit, Department of

Natural Resources, Cornell University, Ithaca, NY, USA

Lesser, V., D. Yang, and L. Newton. 2011. Assessing hunters’ opinions based on a mail

and a mixed-mode survey. Human Dimensions of Wildlife 16:164-173.

McInenly, L. 2013. 2012 Minnesota deer harvest report. In: 2012 Hunting harvest

statistics. Pg. 63-105. Minnesota Department of Natural Resources, St. Paul, MN,

USA.

Messer, B. L., and D. A. Dillman. 2011. Surveying the general public over the Internet

using address-based sampling and mail contact procedures. Public Opinion

Quarterly 75:429-457.

Miles, P. M., D. Heinzen, M. E. Mielke, C. W. Woodall, B. J. Butler, R. J. Piva, D. M.

Meneguzzo, C. H. Perry, D. D. Gormanson, and C. J. Barnett. 2011. Minnesota’s

Forests 2008. Bulletin NRS-50. US Department of Agriculture, Forest Service,

Northern Research Station, Newton Square, PA, USA.

Millar, M. M., and D. A. Dillman. 2012. Encouraging survey response via smartphones:

effects on respondents' use of mobile devices and survey response rates. Survey

Practice 5.

Millar, M. M., and D. A. Dillman. 2011. Improving response to web and mixed-mode

surveys. Public Opinion Quarterly 75:249-269.

Mould, E. D., and C. T. Robbins. 1982. Digestive capabilities in elk compared to white-

tailed deer. Journal of Wildlife Management 46:22-29.

Mower, K. J., T. W. Townsend, and W. J. Tyznik. 1997. White-tailed deer damage to

experimental apple orchards in Ohio. Wildlife Society Bulletin 25:337-343.

Poudyal, N. C., J. M. Bowker, G. T. Green, and M. A. Tarrant. 2012. Supply of private

acreage for recreational deer hunting in Georgia. Human Dimensions of Wildlife

17:141-154.

62

Pradhananga, A., M. Davenport, and L. Cornicelli. 2013. 2013 survey of deer

management on private lands in southeast Minnesota. University of Minnesota,

Minnesota Cooperative Fish and Wildlife Research Unit, Department of Fisheries,

Wildlife, and Conservation Biology and Department of Forest Resources.

Revelle, W. 2015. Psych: procedures for personality and psychological research.

Northwestern University, Evanston, IL, USA. Http://CRAN.R-

project.org/package=psych. Version = 1.5.4.

Schouten, B., J. van den Brakel, B. Buelens, J. van der Laan, and T. Klausch. 2013.

Disentangling mode-specific selection and measurement bias in social surveys.

Social Science Research 42:1555-1570.

Schroeder, S., and L. Cornicelli. 2013. Surveys of hunters participating in the 2012 3A

and 3B firearm deer seasons. University of Minnesota, Minnesota Cooperative

Fish and Wildlife Research Unit, Department of Fisheries, Wildlife, and

Conservation Biology.

Sexton, N. R., H. M. Miller, and A. M. Dietsch. 2011. Appropriate uses and

considerations for online surveying in human dimensions research. Human

Dimensions of Wildlife 16:154-163.

Shih, T., and X. Fan. 2009. Comparing response rates in e-mail and paper surveys: a

meta-analysis. Educational Research Review 4:26-40.

Shin, E., T. P. Johnson, and K. Rao. 2012. Survey mode effects on data quality:

comparison of web and mail modes in a U.S. national panel survey. Social

Science Computer Review 30:212-228.

Siemer, W. F., and T. L. Brown. 1993. Public access to private land for hunting in New

York, NY: a study of 1991 landowners. Publication 93-4. Human Dimensions

Research Unit, Department of Natural Resources, College of Agriculture and Life

Sciences, Cornell University, Ithaca, NY.

Smyth, J.D., D.A. Dillman, L.M. Christian, and A.C. O'Neill. 2010. Using the Internet to

survey small town and communities: limitations and possibilities in the early 21st

century. American Behavioral Scientist 53:1423-1448.

Snyder, S. A., M. A. Kilgore, S. J. Taff, and J. M. Schertz. 2008. Estimating a family

forest landowner’s likelihood of posting against trespass. Northern Journal of

Applied Forestry 25:180-185.

Snyder, S. A., M. A. Kilgore, S. J. Taff, and J. Schertz. 2009. Does forest land posted

against trespass really mean no hunter access? Human Dimensions of Wildlife

14:251-264.

Stedman, R. C., P. Bhandari, A. E. Luloff, D. R. Diefenbach, and J. C. Finley. 2008. Deer

hunting on Pennsylvania’s public and private lands: a two-tiered system of

hunters? Human Dimensions of Wildlife 13:222-233.

63

Tivesten, E., S. Jonsson, L. Jakobsson, and H. Norin. 2012. Nonresponse analysis and

adjustment in a mail survey on car accidents. Accident Analysis and Prevention

48:401-415.

Toepoel, V., and P. Lugtig. 2014. What happens if you offer a mobile option to your web

panel? Evidence from a probability-based panel of Internet users. Social Science

Computer Review 32:544-560.

Tzilkowski, W. M., M. C. Brittingham, and M. J. Lovallo. 2002. Wildlife damage to corn

in Pennsylvania: farmer and on-the-ground estimates. Journal of Wildlife


U.S. Census Bureau. 2015. American Fact Finder. September 18.

http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=

bkmk.

U.S. Department of the Interior, U.S. Fish and Wildlife Service, and U.S. Department of

Commerce, U.S. Census Bureau. 2013. 2011 National survey of fishing, hunting,

and wildlife-associated recreation - Minnesota.

U.S. Department of the Interior, U.S. Fish and Wildlife Service, and U.S. Department of

Commerce, U.S. Census Bureau. 2014. 2011 National survey of fishing, hunting,

and wildlife-associated recreation.

Vannieuwenhuyze, J., G. Loosveldt, and G. Molenberghs. 2010. A method for evaluating

mode effects in mixed-mode surveys. Public Opinion Quarterly 74:1027-1045.

Vaske, J. J. 2008. Survey research and analyis: applications in parks, recreation, and

human dimensions. Venture Publishing, State College, PA, USA.

Vaske, J., M. Jacobs, M. Sijtsma, and J. Beaman. 2011. Can weighting compensate for

sampling issues in Internet surveys? Human Dimensions of Wildlife 16:200-215.

Voogt, R. J., and W. E. Saris. 2005. Mixed mode designs: finding the balance between

nonresponse bias and mode effects. Journal of Official Statistics 21:367-387.

Waller, D. M., and W. S. Alverson. 1997. The white-tailed deer: a keystone herbivore.

Wildlife Society Bulletin 25:217-226.

Wildlife Management Institute. 1983. Improving access to private land: a path to wildlife.

Wildlife Management Institute, Washington, D.C., USA.

Woolf, A., and J. L. Roseberry. 1998. Deer management: our profession’s symbol of

success or failure? Wildlife Society Bulletin 26:515-521.

Wright, B. A., and D. R. Fesenmaier. 1990. A factor analytic study of attitudinal structure

and its impact on rural landowners’ access policies. Environmental Management

14:269-277.

Wright, B. A., and D. R. Fesenmaier. 1988. Modeling rural landowners’ hunter access

policies in East Texas, USA. Environmental Management 12:229-236.

64

Wright, B. A., R. A. Kaiser, and J. E. Fletcher. 1988. Hunter access decisions by rural

landowners: an east Texas example. Wildlife Society Bulletin 16:152-158.

Wright, B. A., R. A. Kaiser, and S. Nicholls. 2002. Rural landowner liability for

recreational injuries: myths, perceptions, and realities. Journal of Soil and Water

Conservation 57:183-191.

Yoder, J. 2002. Deer-inflicted crop damage and crop choice in Wisconsin. Human

Dimensions of Wildlife 7:179-196.

65

Appendix A. 2012 Study of deer management on private lands in southeast Minnesota.

2012 STUDY OF DEER MANAGEMENT ON PRIVATE

LANDS IN SOUTHEAST MINNESOTA

A cooperative study conducted by the University of Minnesota for the

Minnesota Department of Natural Resources

Your help on this study is greatly appreciated!

Please return your completed questionnaire in the enclosed

envelope. The envelope is self-addressed and no postage is

required. Thanks!

Minnesota Cooperative Fish and Wildlife Research Unit,

Department of Fisheries, Wildlife, and Conservation Biology

University of Minnesota

St. Paul, Minnesota 55108

66

First, we would like to know about your property

1. How many total number of acres did you own or lease at the end of 2011.

_________ Acres Owned

_________ Acres Leased

2. Please make a “rough” estimate as to how many acres of your property (owned and leased) are in each

of the following categories:

Acres

Owned

Acres

Leased

Land Type

______ ______ Private Residence (house, lawns, associated buildings)

______ ______ Row Crops

______ ______ Hay fields or Pasture

______ ______ Orchards or vineyards

______ ______ Vegetables or other truck crops

______ ______ Woodlands (natural forest or tree plantings)

______ ______ Brushland (including abandoned, overgrown fields)

______ ______ Wetlands

______ ______ Lands enrolled in State or Federal Conservation Programs

______ ______ Other (please list:__________________________________)

Next we would like to understand how you manage hunting on your land.

3. Is your property posted? Posting means signs displayed on the property line that indicate the land is

private.

Yes

No PLEASE SKIP TO QUESTION 6

4. Please indicate whether you agree or disagree with the following reasons for posting your property:

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(A) Control who uses my land 1 2 3 4 5 6 7

(B) Human safety 1 2 3 4 5 6 7

(C) Liability concerns 1 2 3 4 5 6 7

(D) Eliminate trespass 1 2 3 4 5 6 7

(E) Keep wildlife for myself/family/friends 1 2 3 4 5 6 7

(F) Reduce property damage 1 2 3 4 5 6 7

(G) Livestock safety 1 2 3 4 5 6 7

(H) Relationship with neighbor 1 2 3 4 5 6 7

(I) Better control of deer population 1 2 3 4 5 6 7

(J) Family tradition 1 2 3 4 5 6 7

(K) Conflict with other recreational users 1 2 3 4 5 6 7

(L) Other (please describe): 1 2 3 4 5 6 7

67

5. Did a single event cause you to post your property? (Check only one)

Yes. If yes, please choose the one letter (select one from A through L) from question 4 that

best describes why you posted your property: ________

No

6. Did you allow hunting on your property during the 2011 deer season? (Check only one)

Yes

NoPLEASE SKIP TO QUESTION 9

7. Who did you allow to hunt deer on your property? (Check all that apply). Please also estimate the

number of people who hunted your property in 2011.

Myself or family members _____ people

Friends or neighbors _____ people

Strangers who ask permission _____ people

Specific groups of people who are affiliated with an organized hunting group _____ people

People who lease my property _____ people

Other (please list: _______________________________) _____ people

8. Please indicate if you impose any deer harvest restrictions on your property. (Please check one only)

Antlerless harvest is restricted, but hunters can take any legal buck

Buck harvest is restricted to only large antlered bucks, but hunters can take any antlerless deer

Buck harvest restricted to only large antlered bucks, and antlerless harvest is also restricted

No restrictions on the type of deer that can be harvested

Don't know

Other (please list: ________________________________________________________)

9. To what extent do you agree or disagree with the following statements regarding your decision about

allowing or not allowing deer hunting on your property. (Please circle one number for each statement)

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Hunting will reduce the number of deer

on my property. 1 2 3 4 5 6 7

Hunting is a tradition in my family. 1 2 3 4 5 6 7

I feel pressure from my neighbors to

allow hunting. 1 2 3 4 5 6 7

Hunting will reduce the number of

mature bucks on my property. 1 2 3 4 5 6 7

Allowing other hunters on my property

will reduce my or my family’s

opportunity to hunt deer.

1 2 3 4 5 6 7

Hunters cause too many problems. 1 2 3 4 5 6 7

I am concerned about the liability of

other hunters on my property. 1 2 3 4 5 6 7

I am opposed to deer hunting in general. 1 2 3 4 5 6 7

68

I am not opposed to hunting, but I want

to provide a refuge for deer. 1 2 3 4 5 6 7

Hunting reduces my privacy. 1 2 3 4 5 6 7

Hunting reduces damage caused by deer


Hunting improves the quality of habitat


Hunting on my property will help keep

deer from being over-abundant in the

area.

1 2 3 4 5 6 7

Letting others hunt on my property

encourages a hunting tradition. 1 2 3 4 5 6 7

Hunting puts my livestock at risk. 1 2 3 4 5 6 7

10. To what extent do you agree or disagree with the following statements about your future decisions

about allowing other people to hunt deer on your property. (Please circle one number for each

statement below).

I would be more likely to allow or

continue to allow other people to deer

hunt on my property if…

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The hunters would help me out by

working on the property.

1 2 3 4 5 6 7

I felt like they were interested in getting

to know me and understanding what I’m

trying to do on my property.

1 2 3 4 5 6 7

I knew that they were safe and ethical

hunters.

1 2 3 4 5 6 7

The hunters or an outfitter would pay me

in order to hunt.

1 2 3 4 5 6 7

The Minnesota DNR would pay me to

allow others to hunt.

1 2 3 4 5 6 7

They follow the rules I have for hunting

on my property.

1 2 3 4 5 6 7

11. Do you lease any of your property for deer hunting?

Yes


69

12. Please indicate to the extent you agree or disagree regarding your decision to lease your property to

deer hunters. (Please circle one number for each statement below).

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I have better control over who is using my

land. 1 2 3 4 5 6 7

I have better control over the type of deer

that are harvested. 1 2 3 4 5 6 7

I am managing my property for mature

bucks. 1 2 3 4 5 6 7

Leasing allows me to earn extra money

from my property. 1 2 3 4 5 6 7

I feel pressure from my neighbors who also

lease their property. 1 2 3 4 5 6 7

I see leasing as the future way landowners

can manage their property 1 2 3 4 5 6 7

Other (please indicate): 1 2 3 4 5 6 7

In the next section we have questions about your deer hunting participation in Minnesota.

13. To what extent would you support or oppose a regulation that would increase the proportion of

antlered bucks in the deer area you hunt most often? (Please check one only).

Strongly Oppose

Moderately Oppose

Neither Oppose nor Support

Moderately Support

Strongly Support

Don’t Know

14. Please check the boxes below to report when you hunted deer in Minnesota during the 2009, 2010 or

2011 Minnesota deer season? (Please check all that apply).

2009 Archery | Firearm | Muzzleloader



I DID NOT HUNT ANY OF THESE YEARSPLEASE SKIP TO QUESTION 20 BELOW

15. Which ONE deer permit area did you hunt most often during the most recent deer season you hunted

(check one):

338 | 339 | 341 | 342 | 343 | 344 | 345 | 346 | 347 | 348 | 349 | 602

If you did not hunt one of the permit areas listed above, please tell us which one you did hunt: ______

70

16. How much of your deer hunting did you do on each of the following types of land during your most

recent deer hunting season? (Circle one number for each item).

The next section will address deer populations and harvest management strategies in southeastern

Minnesota. Please answer the questions to the best of your ability, even if you are not entirely

familiar with the deer regulations. The regulations we refer to in this survey include:

A 4-point to one side antler point restriction regulation for all deer seasons

A prohibition on buck cross-tagging

The 3A season was lengthened to 9 days (from 7).

Youth hunters (17 or younger) are exempt from the regulation and can take any buck

17. The regulations that were put in place in southeastern Minnesota in 2010 were designed to put more

harvest pressure on antlerless deer and at the same time protect a large percentage of yearling bucks.

In thinking back to when the regulations were announced before the 2010 deer season, please indicate

your level of support at that time (again, prior to the 2010 deer season).

Strongly Opposed

Moderately Opposed

Neither Opposed nor Supported

Moderately Supported

Strongly Supported

Don’t Know

18. After hunting under the antler point restriction regulations, please indicate whether or not your overall

satisfaction with your hunting experience in southeastern Minnesota may have changed over time.

(Circle one response)

1 2 3 4 5 6 7

Much Less

Satisfied

Somewhat

Less

Satisfied

Slightly

Less

Satisfied

No

Change

Slightly

More

Satisfied

Somewhat

More Satisfied

Much

More

Satisfied

19. After hunting under the antler point restriction regulations, please indicate whether or not your support

for antler point restrictions in southeastern Minnesota may have changed over time. (Circle one

response)

1 2 3 4 5 6 7

Much Less

Support

Somewhat

Less

Support

Slightly

Less

Support

No Change Slightly

More

Support

Somewhat

More

Support

Much

More

Support

None Some Most All Don’t Know

Private land that I own 1 2 3 4 9

Private land that I lease for hunting 1 2 3 4 9

Private land that I do not own or lease 1 2 3 4 9

Public land 1 2 3 4 9

71

20. While no decision has been made to continue the following deer hunting regulations, please indicate

your level of support for continuation of the regulations that were enacted in 2010. (Please circle one

number for each item).

21. Including 2011, how many years have you been hunting deer in Minnesota? ______ Years.

Please check this box if you have not hunted deer in Minnesota.

Next we would like to know about crop and other damage caused by wildlife on your property.

22. Did you experience deer damage to crops on lands that you own or leased in 2011?

Yes


Don’t have cropsPLEASE SKIP TO QUESTION 28

23. How would you describe the total amount of deer damage you experienced in 2011?

Negligible

Minor

Moderate

Severe

Don’t Know

24. How would you compare the amount of deer damage you experienced in 2011 to what you

experienced 5 years ago?

Much less damage in 2011 than 5 years ago

Slightly less damage in 2011 than 5 years ago

About the same damage in 2011 than 5 years ago

Slightly more damage in 2011 than 5 years ago

Much more damage in 2011 than 5 years ago

I was not farming 5 years ago

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Keeping the 3A season at 9 days 1 2 3 4 5 6 7 9

Continue the 4-point to one side

antler point restriction

1 2 3 4 5 6 7 9

Continue the prohibition of buck

cross-tagging

1 2 3 4 5 6 7 9

Exemption of youth from the

antler point restriction

1 2 3 4 5 6 7 9

72

25. In addition to deer, please indicate which other species caused damage to your crops in 2011. (check

all that apply)

Raccoon

Turkey

Geese

Small rodents (mice, voles)

Gophers/Woodchucks

Other (please list: ______________________________________________________)

26. Of all the damage you attributed to wildlife in 2011, what percentage do you feel was due to deer?

(Please circle one percentage).

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

27. In the table below, for each type of crop you grew in 2011, please provide your best estimate of the

total acres you grew, the dollar value of deer damage to that crop, and the estimated percentage of the

total crop value lost to deer damage.

Crop

Acres grown,

2011

Estimated dollar loss from

deer damage to crop

Estimated percent of

total crop value lost

to deer damage

Corn _____ acres $ __________ _______% lost

Soybeans _____ acres $ __________ _______% lost

Alfalfa _____ acres $ __________ _______% lost

Other hay _____ acres $ __________ _______% lost

Tree fruits _____ acres $ __________ _______% lost

Grapes _____ acres $ __________ _______% lost

Stored Forage _____ acres $ __________ _______% lost

Nursery Products _____ acres $ __________ _______% lost

Vegetables _____ acres $ __________ _______% lost

Other: ________________ _____ acres $ __________ _______% lost

TOTALS _____ acres $ __________ _______% lost

In the next section we would like to understand your preferences for deer management.

28. Would you say you know A GREAT DEAL, A MODERATE AMOUNT, A LITTLE, OR NOTHING

about deer management in southeastern Minnesota? (Please check one only).

A GREAT DEAL - I read most of the hunting handbook, DNR news releases, follow the outdoor

media, and am very familiar with the Zone 3 deer season changes

A MODERATE AMOUNT - I read parts of the handbook and occasionally follow the outdoor

media

A LITTLE - I only read the parts that pertain to me and otherwise don't follow the outdoor media

NOTHING - I buy my license but I am not following the southeast deer management issue

DON'T KNOW

73

29. Over the past 5 years, what trend have you seen in the deer population in the area of your property?

More deer now than 5 years ago

About the same number of deer now as 5 years ago

Fewer deer now than 5 years ago

Don’t know

30. In thinking about your property and the surrounding area, would you say the deer population is,

Too high

About right

Too low

Don’t know

31. In thinking about your property and the surrounding area, at what level do you think the deer

population should be managed? (Please circle one)

1 2 3 4 5 6 7

Decrease

50%

(Significant)

Decrease

25%

(Moderate)

Decrease

10%

(Slight)

No Change Increase

10%

(Slight)

Increase

25%

(Moderate)

Increase 50%

(Significant)

32. During the last 5 years, about how many deer were killed on your property each year?

___________number of bucks/antlered deer each year

___________number of does/antlerless deer each year

33. How many deer would you prefer to have killed on your property each year?

___________number of bucks/antlered deer each year

___________number of does/antlerless deer each year

34. When you think about deer management in your area, to what extent do you agree or disagree with the

following statements?

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It is my personal responsibility to manage deer

populations. 1 2 3 4 5 6 7

Landowners in my community should be

responsible for managing deer populations. 1 2 3 4 5 6 7

The Minnesota DNR should be responsible for

managing deer populations. 1 2 3 4 5 6 7

The Minnesota DNR should be responsible for

talking to community members about

managing deer populations. 1 2 3 4 5 6 7

It is my personal responsibility to talk to others

in my community about deer management. 1 2 3 4 5 6 7

Landowners in my community should talk to

each other about managing deer populations. 1 2 3 4 5 6 7

The deer populations in my community are

well managed. 1 2 3 4 5 6 7

74

35. With 1 being your most preferred and 6 being your least preferred, please rank (from 1 to 6) the

following strategies that could be implemented to lower deer populations.

_____ Earn-A-Buck. This would require all deer hunters to kill an antlerless deer before killing an

antlered buck. Under this regulation, hunters cannot shoot a buck until they first killed an antlerless deer.

As a result, harvest rates on antlerless deer will increase.

_____ Buck License Lottery. The annual firearm license would be valid for antlerless deer only.

Hunters interested in killing antlered bucks would need to apply for a permit through a lottery system.

Only lottery winners would be eligible to hunt antlered deer. Unsuccessful applicants would be restricted

to hunting antlerless deer during the current year, but would gain preference points in the lottery which

would improve their chance of getting drawn for a buck license in future years. A hunter would likely win

a buck permit every 2-3 years depending on hunting pressure.

_____ Antler Point Restriction. This regulation has been used since 2010. Only bucks with at least one

4-point antler would be legal to harvest. Hunters could take any antlerless deer.

_____ Early Antlerless Season. This regulation has been used since 2005. A 2-day antlerless only

season would be implemented over the MEA weekend and deer taken during this season would not count

against the annual bag limit.

_____ Limited Depredation Permits. These are permits issued to landowners that could be used during

the deer season. The permits would be valid for antlerless deer only and only on specified private lands.

_____ Localized special seasons. These would be firearms hunts on private lands before or after the

regular firearms season.

36. One idea for lowering deer populations in local areas is to develop localized special seasons. In

general please let us know what you think about such seasons. Please circle one response to indicate

how much you agree or disagree with each statement.

Do you agree or disagree with the

following…

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In general I support the idea of firearms

hunts on private lands either before or

after the regular season. 1 2 3 4 5 6 7

I would prefer that such a season be

before the regular firearm deer season

in late summer (August- Sept.) 1 2 3 4 5 6 7


before the regular firearm deer season

in early fall (mid-Sept – early Oct.) 1 2 3 4 5 6 7


after the muzzleloader deer season

(mid-Dec.) 1 2 3 4 5 6 7


after all the seasons are over (January) 1 2 3 4 5 6 7

75

37. To what extent do you agree or disagree with the following statements about managing your land, use

of wildlife and how you see your community.

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Managing deer on my land is something

I rarely think about. 1 2 3 4 5 6 7

Being a good private land wildlife

steward is an important part of who I

am. 1 2 3 4 5 6 7

I often think of myself as a good private

land wildlife steward. 1 2 3 4 5 6 7

Managing deer and other wildlife on

my land is central to who I am. 1 2 3 4 5 6 7

I feel strongly attached to the

community I live in. 1 2 3 4 5 6 7

There are many people in my

community who I think of as good

friends. 1 2 3 4 5 6 7

I often talk about my community as

being a great place to live. 1 2 3 4 5 6 7

Humans should manage fish and

wildlife populations so that humans

benefit. 1 2 3 4 5 6 7

We should strive for a world where

there’s an abundance of fish and

wildlife for hunting and fishing. 1 2 3 4 5 6 7

Hunting does not respect the lives of

animals. 1 2 3 4 5 6 7

The needs of humans should take

priority over fish and wildlife

protection. 1 2 3 4 5 6 7

Fish and wildlife are on earth primarily

for people to use. 1 2 3 4 5 6 7

Hunting is cruel and inhumane to the

animals. 1 2 3 4 5 6 7

People who want to hunt should be

provided the opportunity to do so. 1 2 3 4 5 6 7

It is acceptable for people to kill

wildlife, if they think it poses a threat to

their life. 1 2 3 4 5 6 7

It is acceptable for people to kill

wildlife, if it poses a threat to their

property. 1 2 3 4 5 6 7

38. What is your gender?

Male

Female

39. What was your total household income before taxes last year? $ _________________________

40. What year were you born? __________YEAR

76

41. What is the highest level of formal education you have completed? (Please check one).

Grade school Some college

Some high school Four-year college (bachelor’s)

High school diploma or GED Some graduate school

Some vocational or technical school Graduate/Professional degree

Vocational or technical school (associate’s)

Please write any additional comments you might have in the space below:

Thank you for your help!

Please complete the survey and mail it back in the enclosed self-addressed envelope. No

postage is necessary.

If you have any questions about the survey please contact Dr. David Fulton, Department of

Fisheries and Wildlife, 1980 Folwell Ave, 200 Hodson Hall, St. Paul, MN 55108 Phone: (612)

625-5256 or Amanda Sames by e-mail at [email protected]

77

Appendix B. 2015 Survey of Minnesota Deer Hunters.

2015 Survey of Minnesota Deer Hunters (H1): Hunters

Opinions and Activities

A cooperative study conducted by the University of Minnesota

for the Minnesota Department of Natural Resources

Your help on this study is greatly appreciated!

Please return your completed questionnaire in the enclosed envelope. The envelope is

self-addressed and no postage is required. Thanks!

Minnesota Cooperative Fish & Wildlife Research Unit,

1980 Folwell Ave., 200 Hodson Hall

Department of Fisheries, Wildlife and Conservation Biology

University of Minnesota

St. Paul, MN 55108

78

Part I. Goal-Setting Survey

1. Please check the boxes below to report if you hunted deer in Minnesota during the 2012, 2013 or 2014

Minnesota deer season. (Please check all that apply).

2012 | 2013 | 2014

I did not hunt deer any of these years PLEASE SKIP TO QUESTION 13

2. Minnesota allows people to hunt deer during all 3 seasons. For the most recent year you hunted, which

seasons did you participate? Please mark ‘Yes’ if you hunted a season and also estimate the number of

days you scouted and hunted.

Season

Yes

No

If Yes,

Number of Days

Scouting

If Yes,

Number of Days

Hunting

Archery ________ ________

Firearm ________ ________

Muzzleloader ________ ________

3. Which ONE deer permit area did you hunt most often during the most recent deer season you hunted?

201 | 203 | 208 | 209 | 213 | 214 | 215 | 218 | 239 | 240 | 256 |

257 | 260 | 261 | 262 | 263 | 264 | 265 | 266 | 267 | 268 | 269 |

270 | 271 | 272 | 273 | 276 | 277 | 297 I hunted a permit area not listed

4. If you did not hunt one of the permit areas listed above, please tell us which one you hunted most

often:

__________Area Number

5. Including 2014, how many years have you hunted deer in the permit area you hunt most often?

______ Years

6. Including 2014, how many years have you been hunting deer in Minnesota? ______ Years

7. How much of your deer hunting did you do on each of the following types of land during your most

recent deer hunting season? (Please circle one item from each row.)

None Some Most All

Private land that I own 1 2 3 4

Private land that I lease for hunting 1 2 3 4

Private land that I do not own or lease 1 2 3 4

Public land 1 2 3 4

79

8. Please indicate if there are any deer harvest restrictions on the property you hunt most often.

Antlerless harvest is restricted, but hunters can take any legal buck

Buck harvest restricted to large antlered bucks, but hunters can take any antlerless deer

Buck harvest restricted to large antlered bucks, and antlerless harvest is also restricted

No restrictions on the type of deer that can be harvested

Other (please explain): ____________________________________________________

9. Please indicate whether you agree or disagree with the following statements regarding your most

recent deer hunt. (Please circle one number for each statement below).

Strongly

Disagree

Slightly

Disagree

Neither

Agree

nor

Disagree

Slightly

Agree

Strongly

Agree

I was satisfied with the number of legal

bucks 1 2 3 4 5

I was satisfied with the quality of bucks 1 2 3 4 5

I heard about or saw legal bucks while

hunting 1 2 3 4 5

I was satisfied with the number of

antlerless deer 1 2 3 4 5

I was satisfied with the number of deer I

saw while hunting 1 2 3 4 5

10. Will you shoot an antlerless deer if given the opportunity?

Yes No

11. Over the past 5 years, what trend have you seen in the deer population in the permit area you hunt most

often?

Much fewer deer now than 5 years ago

Slightly fewer deer now than 5 years ago

About the same number of deer as 5 years ago

Slightly more deer now than 5 years ago

Many more deer now than 5 years ago

12. In thinking about the deer permit area you hunt, please indicate your overall satisfaction with current

deer numbers.

Very Dissatisfied

Slightly Dissatisfied

Neither Dissatisfied nor Satisfied

Slightly Satisfied

Very Satisfied

80

13. How much importance should we assign to each of the following considerations when setting deer

population goals? (Please circle one number for each statement below).

14. Please identify up to 3 other factors that you believe are important and should be considered when

setting deer population goals.

1) _______________________________________________________________________

2) _______________________________________________________________________

3) _______________________________________________________________________

15. In thinking about the deer permit area you hunt, would you say the deer population is,

Much too Low Too Low About Right Too High Much too High

16. In thinking about the property you hunt and the surrounding area, at what level do you think the deer

population should be managed? (Please circle one).

1 2 3 4 5 6 7

Decrease

Population

50%

(Significant)

Decrease

Population

25%

(Moderate)

Decrease

Population

10%

(Slight)

No Change Increase

Population

10%

(Slight)

Increase

Population

25%

(Moderate)

Increase

Population

50%

(Significant)

No

t a

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Imp

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an

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Amount of deer mortality during an average

winter

1 2 3 4 5

Amount of deer mortality during a severe

winter

1 2 3 4 5

Potential health risks to the deer herd 1 2 3 4 5

Public health (human-deer diseases) 1 2 3 4 5

Amount of crop damage from deer 1 2 3 4 5

Number of deer-vehicle collisions 1 2 3 4 5

Deer over-browsing of forests 1 2 3 4 5

Impacts of deer on other wildlife species 1 2 3 4 5

Deer hunting heritage and tradition 1 2 3 4 5

Hunter satisfaction with deer numbers 1 2 3 4 5

Public satisfaction with deer numbers 1 2 3 4 5

Impact of deer hunting on the local economy 1 2 3 4 5

81

17. To what extent would you support or oppose a regulation that would increase the proportion of

antlered bucks in the deer area you hunt most often?

Strongly Oppose

Slightly Oppose

Neither Oppose nor Support

Slightly Support

Strongly Support

Part II. Extended Survey

18. Would you say you know a great deal, a moderate amount, a little, or nothing about DNR’s deer

management program? (Check one).

A great deal – For example, I read most of the hunting handbook, DNR news releases, and/or

follow the outdoor media

A moderate amount – For example, I read parts of the handbook and/or occasionally follow the

outdoor media

A little – For example, I only read the parts of the handbook that pertain to me and don’t follow

the outdoor media

Nothing – For example, I buy my license just before the season and follow the advice of my

friends

19. Indicate how strongly you agree or disagree with the following statements about steps in setting deer

population goals. (Please choose only one response for each statement.)

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It is important for hunters to have opportunities to provide

input regarding population goals 1 2 3 4 5

It is important for landowners to have opportunities to

provide input regarding population goals 1 2 3 4 5

It is important for Minnesotans to have opportunities to

provide input regarding population goals 1 2 3 4 5

It is important to use the best available science when

setting population goals 1 2 3 4 5

It is important to consider diverse interests when setting

population goals 1 2 3 4 5

It is important to follow consistent decision-making

procedures when setting population goals 1 2 3 4 5

It is important that decision-makers explain different

options considered when deer population goals are set,

and why the final option was selected

1 2 3 4 5

82

20. Indicate how strongly you agree or disagree with the following statements about the approach used by

the Minnesota Department of Natural Resources to set deer population goals. (Please choose only one

response for each statement.)

21. Which one of the following best describes how you deer hunted deer during the 2014 regular firearms

deer hunting season in Minnesota? Would you say you (Check only one):

Hunted for large antlered bucks during the entire season

Hunted for large antlered bucks early season and any legal deer later

Would shoot any antlered buck

Would shoot the first legal deer (either antlered or antlerless) that offered a good shot

Would shoot only antlerless deer

Chose not to harvest a deer due to population concern

22. Which statement best characterizes where you hunt?

I almost never hunt the same area every year

I change my hunting location every 1 to 2 years

I change my hunting location every 3 to 5 years

I typically hunt the same area every year

23. If you hunt private land, what size is the parcel you typically hunt? ___________ acres

24. Do you cooperate with other deer hunters on nearby properties with respect to deer harvest

restrictions, so that there are similar strategies in place in the area you hunt?

Yes No

25. Which techniques did you use to hunt during the most recent year you hunted? Check each item that

applies.

Stand hunting from ground stand/blind

Stalking or moving slowly

Hunting from elevated tree stand

Participated in deer drives as member of a party

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DNR provides enough opportunities for hunters to have input

regarding population goals 1 2 3 4 5

DNR provides enough opportunities for landowners to have


DNR provides enough opportunities for Minnesotans to have


DNR provides adequate information for the public to provide


DNR considers the best available science when setting

population goals 1 2 3 4 5

DNR follows consistent decision-making procedures when

setting population goals 1 2 3 4 5

DNR explains different options considered when deer

population goals are set, and why the final option was

selected

1 2 3 4 5

I trust the DNR to establish appropriate deer population goals 1 2 3 4 5

83

26. During the 2014 Minnesota deer season, did you,

27. Please indicate how much you support or oppose the following potential changes to deer hunting

regulations in Minnesota.

28. Below is a series of 8 hypothetical scenarios for potential combinations for deer seasons and

regulations. We are interested in your preferences for potential deer hunting regulations in Minnesota.

Some of these scenarios may seem unlikely, and there are no specific changes planned at this time.

(For each scenario, select the one choice with the characteristics you would prefer.)

Scenario 1.

Option 1

Cross-tagging illegal for both

sexes

Antler point restrictions

Late November opener (out of the

rut)

Deer numbers higher than current

levels

Two deer limit (Managed)

Option 2

Cross-tagging legal for either sex

No antler point restrictions

Early November opener (during

the rut)

Deer numbers lower than current

levels

One deer limit, either sex (Hunter

Choice)

NONE: I

would not

hunt deer

in MN with

these

options.

Check one

box ►

Yes No

Kill and tag an antlerless deer

Kill and tag a legal buck

Kill a deer for another hunter (a member of your party tagged the

deer you killed)

Use your tag on a deer that another hunter killed?

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Delay the firearm deer season one week. The deer season

would open the Saturday closest to November 13th.

Currently, the season opens the Saturday closest to

November 6th, which is about one week prior to peak rut.

1 2 3 4 5

Delay the firearm deer season until late November. The

deer season would open the Saturday closest to November

20th.

1 2 3 4 5

Institute an antler point restriction. This would be for adult

hunters only. Youth hunters could still take any deer. 1 2 3 4 5

Eliminate buck cross-tagging. People would still be allowed

to hunt as a party but hunters would be required to shoot and

tag their own buck. Hunters would still be allowed to shoot

and tag antlerless deer for each other.

1 2 3 4 5

Eliminate cross-tagging for bucks and antlerless deer. 1 2 3 4 5

84

Scenario 2.

Option 1

Cross-tagging legal for either

sex


Late November opener (out of

the rut)

Deer numbers higher than

current levels


Option 2

Cross-tagging legal for antlerless

only



rut)

Deer numbers at current levels

One deer limit, antlerless by

permit only (Lottery)

NONE: I

would not

hunt deer in

MN with

these

options.

Check one

box ►

Scenario 3.

Option 1


only



rut)




Option 2


sexes



rut)


levels


Choice)

NONE: I

would not

hunt deer

in MN with

these

options.

Check one

box ►

Scenario 4.

Option 1




rut)


levels



Option 2


sexes



rut)


levels


Choice)

NONE: I

would not

hunt deer

in MN with

these

options.

Check one

box ►

Scenario 5.

Option 1


only



rut)


levels


Option 2


only



the rut)



Choice)

NONE: I

would not

hunt deer

in MN with

these

options.

Check one

box ►

85

Scenario 6.

Option 1


sexes



rut)



Option 2




the rut)


levels


NONE: I

would not

hunt deer

in MN with

these

options.

Check one

box ►

Scenario 7.

Option 1


only



the rut)

Deer numbers higher than

current levels



Option 2


sexes



rut)

Deer numbers lower than

current levels



NONE: I

would not

hunt deer

in MN with

these

options.

Check one

box ►

Scenario 8.

Option 1


only



rut)


One deer limit, either sex

(Hunter Choice)

Option 2


sexes



the rut)



NONE: I

would not

hunt deer

in MN with

these

options.

Check one

box ►

86

29. Please indicate how much you agree or disagree with the following statements about your involvement

in deer hunting in Minnesota. (Please circle one response for each):

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Deer hunting is one of the most enjoyable things I do. 1 2 3 4 5

Deer hunting provides me with the opportunity to be with friends. 1 2 3 4 5

To change my preference from deer hunting to another recreation

activity would require major rethinking. 1 2 3 4 5

A lot of my life is organized around deer hunting. 1 2 3 4 5

Deer hunting has a central role in my life. 1 2 3 4 5

Most of my friends are in some way connected with deer hunting. 1 2 3 4 5

When I am deer hunting, others see me the way I want them to see

me. 1 2 3 4 5

I identify with the people and images associated with deer

hunting. 1 2 3 4 5

Deer hunting is one of the most satisfying things I do. 1 2 3 4 5

Participating in deer hunting says a lot about who I am. 1 2 3 4 5

Deer hunting is very important to me. 1 2 3 4 5

You can tell a lot about a person when you see them deer hunting. 1 2 3 4 5

When I am deer hunting I can really be myself. 1 2 3 4 5

I enjoy discussing deer hunting with my friends. 1 2 3 4 5

When I am deer hunting, I don’t have to be concerned about what

other people think of me. 1 2 3 4 5

I contribute to deer management through hunting. 1 2 3 4 5

30. During the 2014 Minnesota deer hunting season, how satisfied or dissatisfied were you with the

following?

Very

dissatisfied

Slightly

dissatisfied Neither

Slightly

satisfied

Very

satisfied

General deer hunting experience 1 2 3 4 5

Deer hunting harvest 1 2 3 4 5

Deer hunting regulations 1 2 3 4 5

Number of other deer hunters seen 1 2 3 4 5

31. Overall, how satisfied were you with your 2014 deer hunt?

Very dissatisfied

Slightly dissatisfied

Neither satisfied or dissatisfied

Slightly satisfied

Very satisfied

87

32. Please tell us how important each of the following experiences was to your deer hunting satisfaction

during the 2014 season.

33. How would you best describe your identification with the activity of deer hunting (While more than

one option may apply to you, please select one choice)

I am a recreational deer hunter – I hunt to get away from my regular routine, enjoy nature and

an outdoor recreation experience.

I am a meat hunter – I hunt to provide food for my family and friends. Venison is an important

part of our annual diet.

I am a trophy hunter – I hunt for the challenge of harvesting a large buck. The opportunity to

see or harvest a trophy animal is more important to me than tagging a deer every year.

I am a social deer hunter – The companionship of the hunt is most important to me. Hunting

gives me an opportunity to spend time with family and friends.

I am a science-oriented hunter – I spend a significant amount of time reading about deer

behavior and deer management. I look for the most up-to-date research and expertise to inform

my hunt. I make hunting decisions based on deer management objectives.

I am a skills-oriented hunter – Hunting is a way to test and improve my skills. I enjoy the

challenge of using new equipment and spend considerable time practicing to become more

proficient.

I am a casual/occasional deer hunter – I enjoy deer hunting but don’t go every year. I don’t

spend a lot of time preparing for my season.

Not at all

important

Slightly

important

Somewhat

important

Very

important

Extremely

important

Being with hunting companions 1 2 3 4 5

The challenge of harvesting a trophy buck 1 2 3 4 5

Developing my skills and abilities with

hunting equipment 1 2 3 4 5

Becoming a better deer hunter 1 2 3 4 5

Influencing deer sex ratios or age structures 1 2 3 4 5

Hunting with friends 1 2 3 4 5

Improving my knowledge about deer and

deer management 1 2 3 4 5

Getting food for my family 1 2 3 4 5

Harvesting any deer for meat 1 2 3 4 5

Enjoying a preferred pastime 1 2 3 4 5

Harvesting any buck 1 2 3 4 5

Enjoying nature and the outdoors 1 2 3 4 5

Helping manage deer populations 1 2 3 4 5

Getting a buck every year 1 2 3 4 5

Hunting with family 1 2 3 4 5

Seeing a lot of bucks 1 2 3 4 5

Harvesting a large buck 1 2 3 4 5

Proving my hunting skills and knowledge 1 2 3 4 5

Harvesting at least one deer 1 2 3 4 5

Selectively harvesting a large buck even if

it means not killing a deer 1 2 3 4 5

Seeing a lot of deer 1 2 3 4 5

88

34. Currently, Minnesota holds a youth deer season mid-October in portions of northwestern and

southeastern Minnesota with additional special youth hunts held during the same period throughout the

state. Would you support or oppose a statewide youth season in mid-October? (Please circle one.)

1 2 3 4 5

Strongly oppose Oppose Neutral Support Strongly support

35. Currently, Minnesota employs different firearm season lengths statewide (e.g. 100-series season is 16

days while the 200-series is 9 days). If a consistent, statewide season were implemented, which length

would you prefer?

9 days 16 days

36. If the MnDNR were to adopt new deer management regulations, would you prefer to see them applied

(Check one).

Statewide

By Zone (e.g., 100-series, 200-series, 300-series)

By Deer Permit Area

37. Indicate how strongly you agree or disagree with the following statements about the approach used by

the Minnesota Department of Natural Resources to set deer hunting rules. (Please choose only one

response for each statement.)

38. Indicate how strongly you agree or disagree with the following statements about your relationship with

Minnesota DNR as it relates to deer management. (Please choose only one response for each statement.)

Strongly

Disagree

Disagree

Not

Sure

Agree

Strongly

Agree

DNR provides enough opportunities for hunters to have

input regarding hunting rules 1 2 3 4 5

DNR considers the best available science when setting

hunting rules 1 2 3 4 5

DNR follows consistent decision-making procedures when

setting hunting rules 1 2 3 4 5

DNR explains different options considered when deer

hunting rules are set, and why the final option was selected 1 2 3 4 5

I trust the DNR to establish appropriate deer hunting rules 1 2 3 4 5

Strongly

Disagree

Disagree

Not

Sure

Agree

Strongly

Agree

I have adequate opportunities to communicate with DNR staff 1 2 3 4 5

I know who to contact if I have questions or comments 1 2 3 4 5

I have communicated with my local conservation officer 1 2 3 4 5

I know my local conservation officer 1 2 3 4 5

I have communicated with my local wildlife manager 1 2 3 4 5

I know my local wildlife manager 1 2 3 4 5

I have communicated with deer management staff 1 2 3 4 5

I know deer management staff 1 2 3 4 5

89

39. What is your preferred means to provide input on deer management decisions?

General public meetings

Issue-based public meetings

Through a representative organization

Online questionnaires

Written questionnaires

Advisory teams

Informal communication (e.g., telephone)

None

Other _________________________________________________________

40. Are you currently a member of: (Check all that apply.)

Minnesota Deer Hunters Association

Quality Deer Management Association

Local sporting club

Minnesota Whitetail Alliance

Minnesota Bowhunters, Inc

Other national/statewide conservation/hunting organization(s):

Please specify: .

41. Please let us know how you feel about the Minnesota Department of Natural Resources. (Please

circle one response for each of the following statements.)

42. How many years have you lived in Minnesota? Years

43. What is your gender?

Male Female

44. What is your age? __________

45. What is the highest level of education you have completed? (Check one.)

Grade school Some college

Some high school Four-year college (bachelor’s) degree

High school diploma or GED Some graduate school

Some vocational or technical school Graduate (master’s or doctoral) degree

Vocational or technical school (associate’s) degree

46. Do you have access to the internet at home or another location?

Yes No

Strongly

Disagree

Slightly

Disagree

Neither

Agree

nor

Disagree

Slightly

Agree

Strongly

Agree

The MnDNR does a good job of managing deer in

Minnesota. 1 2 3 4 5

When deciding about deer management in Minnesota,

the MnDNR will be open and honest in the things they

do and say.

1 2 3 4 5

The MnDNR can be trusted to make decisions about

deer management that are good for the resource. 1 2 3 4 5

The MnDNR will make decisions about deer

management in a way that is fair. 1 2 3 4 5

The MnDNR has deer managers and biologists who are

well-trained for their jobs. 1 2 3 4 5

The MnDNR listens to the concerns of deer hunters. 1 2 3 4 5

90

If you would be willing to respond to additional questions about deer management and hunting in

Minnesota and are willing to provide your email address, please write it below. We will only use your

email address for research related to deer management and will not share it with anyone.

E-mail address:

___________________________________________________________________________

I do not have an e-mail address

landowner and hunter surveys for white-tailed deer ......landowner and hunter surveys for...

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